New satellite constellations to provide high-resolution atmospheric observations from microwave (MW) sounders operating in low-Earth orbit are now coming online and are providing operationally useful data. The first of these missions, the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 7 and 25, 2023 (Eastern Daylight Time, two CubeSats in each of the two launches). TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS is providing rapid-refresh MW measurements (median refresh rate of better than 60 min early in the mission with four functional CubeSats, and now approximately 70–90 min with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of TCs have been captured thus far by the TROPICS mission, revealing the detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing TC analysis at operational centers. Here, we present an overview of the TROPICS mission after two years of successful science operations with a focus on the suite of geophysical (Level 2) products (atmospheric vertical temperature and moisture profiles, instantaneous surface rain rate, and TC intensity) and the science investigations that have been enabled by these new measurements.

The four NASA TROPICS Earth Venture (EVI-3) CubeSat constellation satellites were successfully launched into orbit on May 8 and May 26, 2023 (NZST) – two satellites were deployed in each launch. TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at a median refresh rate of approximately 60 minutes to conduct high-value science investigations of tropical cyclones. TROPICS provides microwave measurements in twelve channels spanning 90-205 GHz over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of tropical cyclones have been captured thus far by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 91.65 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is informing tropical cyclone analysis at operational centers. In this paper, the radiance and geophysical performance of Pathfinder and the constellation satellites is presented, showing that the mission is on track to meet its baseline requirements.

In this paper, the key models, assumptions, and statistical characterizations/uncertainties are examined to better understand how to evaluate hyper-spectral sampling (HSS) and "multi-angle-sampling (MAS) systems and interpret results that are commonly presented in the contemporary literature. The analysis presented is based on a set of thousands of representative, global atmospheric profiles over a variety of surfaces. These profiles are used with a line-by-line radiative transfer model to calculate the at-sensor radiance for a variety of viewing angles, spectral response functions, channelization schemes, and noise assumptions. A neural-network-based retrieval scheme [Blackwell, 2005] similar to one that is used for AIRS/AMSU on Aqua and for TROPICS near-real-time processing is used to retrieve the profiles. Errors are characterized by bias, RMS uncertainty, averaging kernel width, and retrieved profile error correlation width for a variety of sensor configurations to explore HSS and MAS performance. Neural network jacobians [Blackwell, 2012] facilitate the direct calculation of these metrics in some cases.

We have developed and implemented a near-real-time retrieval algorithm to estimate temperature and water vapor vertical profiles from TROPICS [1] L1b brightness temperature observations. A neural network approach, with heritage in our past work [2] in the operational Atmospheric Infrared Sounder science products [3], was selected due to fast execution time, overall accuracy, and robustness to a wide variety of meteorological conditions. Here, we describe our methodology, present initial performance results on the TROPICS Pathfinder mission on test data sets, and describe our ongoing efforts to validate the algorithm.

Currently, the planetary boundary layer (PBL) is challenging to assess in PBL remote sensing retrievals from space. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in 3D granules of temperature and humidity retrieved from hyperspectral infrared and microwave sounders. We show that this approach improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture-Instrument (EVI-3) program. TROPICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance millimeterwave radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. Spatial resolution at nadir ranges from approximately 15 km for the G-band channels to 30 km for the W-band channels. The Pathfinder (Qualification Unit) was launched on June 30, 2021, and the six constellation flight units are scheduled to launch in the first half of 2022. The TROPICS Pathfinder mission has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission. This presentation will describe the on-orbit results for the successful TROPICS Pathfinder precursor mission [4], and will highlight new observations of cloud ice and snow at 205 GHz.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture--Instrument (EVI-3) program. TROPICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance millimeterwave radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. Spatial resolution at nadir ranges from approximately 15 km for the G-band channels to 30 km for the W-band channels. The Pathfinder (Qualification Unit) was launched on June 30, 2021, and the six constellation flight units are scheduled to launch in the first half of 2022. The TROPICS program has matured several key technologies for small satellite remote sensing using microwave radiometers, including self-calibrating millimeterwave receivers, low-power radiometer systems, low-loss antenna assembly, and compact mechanical scanner. This paper summarizes the key receiver and antenna technologies, proven by the successful Pathfinder mission and slated for further use in the constellation mission [4].

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture-Instrument (EVI-3) program. TROPICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance millimeterwave radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. Spatial resolution at nadir ranges from approximately 15 km for the G-band channels to 30 km for the W-band channels. The Pathfinder (Qualification Unit) was launched on June 30, 2021, and the six constellation flight units are scheduled to launch in the first half of 2022. The TROPICS Pathfinder mission has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission. This presentation will describe the on-orbit results for the successful TROPICS Pathfinder precursor mission and will highlight numerous technical innovations that have made the TROPICS mission possible and enabled new capabilities for future Earth observing missions [4].

CubeSats with miniaturized microwave radiometers are now demonstrating the potential to provide science-quality weather measurements. For example, the Micro-Sized Microwave Atmospheric Satellite-2A (MicroMAS-2A) and Temporal Experiment for Storms and Tropical Systems–Demonstration (TEMPEST-D) CubeSats are both launched in 2018 and have demonstrated microwave atmospheric sounder data from orbit. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a follow-on constellation of six 3U CubeSats based on the MicroMAS-2 design that is scheduled for launch no sooner than 2021. The TROPICS sensors use internal noise diodes (NDs) for calibration. Although the NDs on TROPICS are similar to technology flown on GMI, they have not been tested on-orbit at TROPICS frequencies. In order to track and correct ND drift, we develop a novel method of calibration for CubeSat constellations, such as TROPICS, by incorporating periodic solar and lunar intrusions as an additional source of information to counter ND drift. These lunar intrusions also occur for existing satellites hosting microwave radiometers in sun-synchronous polar orbits but are much more infrequent than for the TROPICS constellation’s scanning payload. In this work, we develop a solar/lunar calibration correction algorithm and test it using advanced technology microwave sounder (ATMS) lunar intrusion data. The mean bias and standard deviation between the solar/lunar calibration correction algorithm and actual ATMS data fall within the expected ATMS error budget of 0.6–3.9 K, validating our model.

Passive microwave sounders provide the highest-impact observations ingested by major numerical weather prediction (NWP) forecast models. The Advanced Technology Microwave Sounder (ATMS), built by Northrop Grumman, Azusa, CA, USA, is the latest operational microwave sounder series being launched by the United States to provide both temperature and water vapor soundings of the atmosphere. The first ATMS was launched on the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011. This article focuses on the details of the on-orbit performance characterization of the second ATMS, which launched on November 18, 2017, on the Joint Polar Satellite System-1 (JPSS-1) satellite. After successful commissioning, JPSS-1 was renamed National Oceanic and Atmospheric Administration (NOAA)-20 (N-20). We present performance characterizations from prelaunch and postlaunch tests, including the thermal vacuum (TVAC) campaign, and postlaunch activities that contribute to the radiance data products. Significant improvements were found for reflector emissivity,$1/f$noise performance, antenna beam efficiency, interchannel noise correlation, and scan drive bearing design. New geolocation and pointing algorithms were evaluated. The N-20 ATMS has the same channel set, polarizations, scan geometry, and calibration approach as the SNPP ATMS. The N-20 ATMS meets all performance requirements with margin.

Over the years, a fixed window for smoothing radiometer cold-space and warm-load counts and processing brightness temperature in calibration has been used for all microwave sounders at EUMETSAT and NOAA. Although this practice is based on ground tests and legacy satellites, it remains unclear if this empirical parameter is optimal for in-orbit radiometers, as the space environment is different from the ground and radiometers may drift. We found that the fixed window is not optimal and leads to large noise.We have developed an adaptive window that accommodates channel differences and temporal changes in hardware. Our method has reduced noise by as much as 50% for 183 GHz channels of MetOp-C MHS. We observed temporal jumps and shifts in counts, gain and noise of 89 and 190 GHz, and accordingly, the adaptive window can adjust to reduce such an impact. Further analyses reveal that 1/fnoise plays an important role for determining the adaptive window. 1/fnoise is non-stationary and gives rise to the fluctuation of counts and gain. As a result, for channels with large 1/fnoise a short window should be used to mitigate the fluctuation. Our study suggests an adaptive method has advantages over the fixed method for considering channel differences and timevarying noise.

Striping has been observed in the MetOp-A microwave humidity sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W-/G-bands of MHS. Here, we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and$1/f$noises, and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz, which has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and$1/f$noises remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that$1/f$noise generates sharp, nonperiodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of$1/f$to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and 190 GHz with the striping percentage over 50% while lower in 183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact on data assimilation and science products.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture–Instrument (EVI-3) program. The TROPICS Engineering Qualification Unit has been refurbished for flight, and a launch is planned for June 2021 on a SpaceX Falcon 9 to a 550-km sun synchronous orbit. This Pathfinder mission will provide risk reduction for the subsequent TROPICS constellation mission, which comprises six CubeSats in three low-Earth low-inclination orbital planes, with launches planned beginning in early 2022. Each of these identical CubeSats will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. The TROPICS mission highlights a number of aspirations of future earth observing sysems, including high revisit rate, system resilience, rapid technology infusion, and low cost. This paper presents these elements with an eye toward future operational architectures for weather and climate monitoring.

This paper describes the TROPICS L1 calibration algorithm. The NASA TROPICS mission [1] hosts a passive microwave radiometer [2] that provides temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. Noise Diodes (ND) are an attractive option for calibration of small satellite sounders, which replaces the bulky and heavy internal iron-filled epoxy calibration target that is difficult to shield from the sun [3]. The TROPICS radiome-ter's pre-launch thermal-vacuum testing characterized the ND output temperature and the radiometer's non-linearity, and also verified the characterization using parts of the algorithm described in this paper[4].

The Microsized Microwave Atmospheric Satellite (MicroMAS-2A) is a 3U CubeSat that was launched in January 2018 as a technology demonstration for future microwave sounding constellation missions, such as the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission now in development. MicroMAS-2A has a miniaturized 1U ten-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging [4]. MicroMAS-2A provided the first CubeSat atmospheric vertical sounding data from orbit, and to date it is the only CubeSat to provide temperature and moisture sounding and surface imaging. In this article, we analyze six segments of data collected from MicroMAS-2A in April 2018 and compare them to ERA5 reanalysis fields coupled with the Community Radiative Transfer Model (CRTM). This initial assessment of CubeSat radiometric accuracy shows biases relative to ERA5 with magnitudes ranging from 0.4 to 2.2 K (with standard deviations ranging from 0.7 to 1.2 K) for the four mid-tropospheric temperature channels and biases of 2.2 and 2.8 K (standard deviations 1.8 and 2.6 K) for the two lower tropospheric water vapor channels.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROP-ICS) mission was selected by NASA as part of the Earth Ven-tureInstrument (EVI-3) program and flight hardware has been delivered for a launch in the 2021-2022 timeframe. TROP-ICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. A seventh (spare) flight unit has also been delivered that could serve as the seventh element in the constellation or could be launched in advance of the primary mission for checkout of key system performance. The TROPICS mission highlights a number of aspirations of future earth observing sysems, including high revisit rate, system resilience, rapid technology infusion, and low cost. This paper presents these elements with an eye toward future operational architectures for weather and climate monitoring.

The NASA TROPICS mission [1] hosts a high performance radiometer to estimate temperature and water vapor profiles, precipitation, and Tropical Cyclone intensity. Noise Diodes (ND) are an attractive option for calibration of small satellite sounders, especially in the face of volume constraints that preclude extensive radiative shielding of internal calibration targets that is utilized in operational missions [2]. Recent operational missions [3], [4] demonstrated on-orbit ND calibration stability better than 0.1 K over more than four years when prelaunch screening of the ND is used. An essential step for TROPICS is the precise prelaunch characterization of the ND performance over a variety of thermal conditions, which will be augmented by post-launch cross-comparisons similar to the techniques used by the GPM Intercalibration (X-CAL) Working Group. This paper introduces the first radiometric calibration and verification with the radiometer integrated on the satellite platform under flight-like conditions.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth VentureInstrument (EVI-3) program and is now in development with planned launch readiness in late 2019. TROPICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. The instrumentation that will fly on this mission has benefitted greatly from several technology development efforts funded by the NASA Earth Science Technology Office (ESTO). This paper provides an overview of these development and demonstration efforts and shows how these enabling technologies are planned for use for the TROPICS mission that will illuminate a new set of science questions relating to tropical cyclone formation, evolution, and forecasting.

In January 2019 a Project Authorization Request was submitted to the IEEE standards association with the title "Standard for Calibration of Microwave Radiometers in the 300 MHz to 1 THz Frequency Range for Geoscience Applications". An open committee is being assembled to draft this standard with the purpose of unifying and documenting calibration procedures for a wide range of microwave radiometers. The committee includes members, collaborators, and contributors from academia, international government and private industry. We include ground-based, air-borne, and space-borne systems. The standard will also define standardized terminology, and address procedures required to obtain traceability to fundamental units or constants. The scope of the standard encompasses various radiometer geometries, Dicke switching, total power, and differential, as well as different polarization configurations including fully polarized (full Stoke's) radiometers. The standard will also separately address free-space and single-mode (e.g. transmission-line) radiometer calibration techniques.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROP-ICS) mission was selected by NASA as part of the Earth Venture Instrument (EVI-3) program. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones, including: (1) relationships of rapidly evolving precipitation and upper cloud structures to upper-level warm-core intensity and associated storm intensity changes; (2) the evolution of precipitation structure and storm intensification in relationship to environmental humidity fields; and (3) the impact of rapid-update observations on numerical and statistical intensity forecasts of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate of 40 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. TROPICS comprises six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 206 GHz that is more sensitive to precipitation-sized ice particles. This observing system offers an unprecedented combination of horizontal and temporal resolution to measure environmental and inner-core conditions for tropical cyclones on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROP-ICS) mission was selected by NASA as part of the Earth VentureInstrument (EVI-3) program and is now in development with planned launch readiness in late 2019. TROPICS comprises a constellation of six CubeSats in three low-Earth low-inclination orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. Expected NEDTs range from approximately 0.5 to 1.0 K for an 8.333 msec integration time. Spatial resolution at nadir ranges from approximately 15 km for the G-band channels to 30 km for the W-band channels.

This paper evaluates the performance of the passive microwave spectrometer to be launched aboard the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) for retrieving surface precipitation and hydrometeor water paths. The retrieval algorithms employ neural networks trained and evaluated using the physical reference model NCEP/WRF/TBSCAT/F( λ). Results show that TROPICS retrieved surface precipitation rates and hydrometeor water paths agree well with WRF truth. The accuracies of TROPICS retrieved daily, weekly, and monthly surface precipitation amounts are close to those of the Advanced Microwave Sounding Unit (AMSU). The TROPICS constellation will provide useful precipitation retrievals at unprecedented 30-minute temporal resolution.

Recent advances in low-power millimeterwave low-noise amplifier technologies have enabled the hosting of high-performance atmospheric sounding instruments on very small satellites. Microwave instrumentation is particularly well suited for such implementations, as the sensor requirements for power, pointing, and spatial resolution (aperture size) can readily be accommodated by a nanosatellite platform. Several missions have been formulated to demonstrate the core technologies. The Microsized Microwave Atmospheric Satellite, second generation (MicroMAS-2), will demonstrate temperature sounding near 118 GHz and moisture sounding near 183 GHz. MicroMAS-2a and MicroMAS-2b are scheduled to launch in 2017. The Microwave Radiometer Technology Acceleration (MiRaTA) cubesat will demonstrate multi-band atmospheric sounding and co-located GPS radio occultation. MiRaTA will launch in 2017, and will fly a tri-band sounder (60, 183, and 206 GHz) and a GPS radio occultation (GPS-RO) sensor. Both MicroMAS and MiRaTA are 3U CubeSats (aggregates of 10 × 10 × 10 cm cubes). The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission utilizes these technology advancements in a complete mission with a constellation of CubeSats similar in capability to MicroMAS-2. TROPICS is expected to launch in 2020. The Earth Observing Nanosatellite-Microwave (EON) concept is a 12U CubeSat designed to provide most of the capabilities of current operational microwave sounders. These new capabilities offer the potential of higher performance and reliability through the use of constellation observatories.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was recently selected by NASA as part of the Earth Venture Instrument (EVI-3) program. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones, including: (1) relationships of rapidly evolving precipitation and upper cloud structures to upper-level warm-core intensity and associated storm intensity changes; (2) the evolution of precipitation structure and storm intensification in relationship to environmental humidity fields; and (3) the impact of rapid-update observations on numerical and statistical intensity forecasts of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate of 30 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm life-cycle. TROPICS comprises approximately 12 CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 206 GHz that is more sensitive to precipitation-sized ice particles. This observing system offers an unprecedented combination of horizontal and temporal resolution to measure environmental and inner-core conditions for tropical cyclones on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data.

The next World Radio Conference (WRC) will be held in November 2019 in Geneva, Switzerland. This paper discusses WRC-19 agenda items that could impact scientific uses in Earth satellite remote sensing and radio astronomy.

In recent years, CubeSats have emerged as a platform of intense interest for a wide range of applications, including remote sensing. Of specific interest in this paper are data processing challenges associated with the MIT's Microwave Atmospheric Satellite (MicroMAS). Due to the motion of MicroMAS and the geometry of the data acquisition process, measurements are not collected on a regular grid of spatial locations as required by most applications. Thus, a fundamental problem in processing these data is that of interpolation. The problem is further complicated by the fact that MicroMAS collects data from several frequencies at a single location. A baseline algorithm that can be used to solve this multi-frequency scattered data interpolation problem is to fit data from each frequency via independent Gaussian Process (GP) and apply standard GP regression to estimate unknown data on the regular grid for each frequency separately. However, this approach ignores the correlation between frequencies. From the covariance structure in the aforementioned Independent Multiple output GP Regression (IMGPR) algorithm, we proposed a Correlated Multiple output GP Regression (CMGPR) algorithm which replaces a set of delta vectors with parameterized weight vectors learned from the dataset. To test the effectiveness of our proposed algorithms, we use NOAA's ATMS temperature data. According to the experimental results, the CMGPR algorithm performs better than the IMGPR.

With the recent development of CubeSats, several ultracompact, low cost, and rapidly deployable satellites have been developed for earth observation missions. Because of the geometry of the acquisition process, measurements are irregularly sampled, whereas in meteorological applications, data are preferred on a regular grid. This problem is further complicated by the fact that, due to CubeSats' compact sizes and constraints, such as limited power, errors occur in geolocation calibration, resulting in positional errors. In this letter, we analyze how the commonly used triangulation-based linear data interpolation scheme behaves under probabilistic models for the positional errors. The derived distribution of interpolation error caused by positional error is intractable even under a Gaussian distribution for positional errors. To address this problem, we developed an analytical closed-form solution to the first two moments of the interpolation error. Using models for positional errors motivated by our prior work, experimental results show that, compared with the first-order linear model, the second-order one provides a better approximation in terms of the mean and variance, which is very close to that is obtained using more computationally intensive Monte Carlo simulations. This model also allows for the closed-form calculation of mean squared interpolation error, which can be of use in the context of system design where the impact of positional errors on remote sensing products must be considered.

The Microwave Radiometer Technology Acceleration (MiRaTA) is a 3U CubeSat mission sponsored by the NASA Earth Science Technology Office (ESTO). The science payload on MiRaTA consists of a tri-band microwave radiometer and GPS radio occultation (GPSRO) experiment. The microwave radiometer takes measurements of all-weather temperature (V-band, 52–58 GHz), water vapor (G-band, 175–191 GHz), and cloud ice (G-band, 207 GHz) to provide key observations used to improve weather forecasting. The GPSRO experiment, called the Compact TEC (Total Electron Content) and Atmospheric GPS Sensor (CTAGS) measures profiles of temperature and pressure in the upper neutral atmosphere and electron density in the ionosphere. The MiRaTA mission will validate new technologies in both passive microwave radiometry and GPS radio occultation: (1) new ultra-compact and low-power technology for multi-channel and multi-band passive microwave radiometers, (2) new GPS receiver and patch antenna array technology for both neutral atmosphere and ionospheric GPS radio occultation retrieval on a nanosatellite, and (3) a new approach to spaceborne microwave radiometer calibration using adjacent GPSRO measurements.

With their greatly reduced sizes, low development cost and rapid construction times, CubeSats have emerged as a platform of intense interest for a wide range of applications, including remote sensing. However, due to their compact form factor, performance tradeoffs relative to larger existing platforms have been encountered. Of specific interest in this paper are data processing challenges associated with the Micro-MAS platform. In meteorological applications, the radiometer samples are preferred on a regularly spaced grid for generating subsequent scientific products such as vertical temperature and water vapor profiles, or fusing with other gridded datasets. However, in reality, MicroMAS radiometer samples are not regularly spaced, and are expected to have geolocation errors comparable in magnitude to the beam-width [10]. In this work, we present a joint maximum a posteriori (MAP) estimation approach to determine both sample locations as well as brightness temperature on a regular spatial grid given irregularly sampled data corrupted by noise and uncertainty in sample locations. The performance of this approach is tested on Advanced Technology Microwave Sounder (ATMS) data which demonstrates significant improvement both qualitatively and quantitatively compared with traditional estimation methods.

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earth's limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015.

The Micro-sized Microwave Atmospheric Satellite (MicroMAS) is a 3U CubeSat (10×10×34 cm, ~4 kg) hosting a passive microwave spectrometer operating near the 118.75-GHz oxygen absorption line. MicroMAS is a dual-spinning 3U CubeSat that aims to address the need for low-cost, mission-flexible, and rapidly deployable spaceborne sensors. The focus of the current MicroMAS mission is to observe convective thunderstorms, tropical cyclones, and hurricanes from a near-equatorial orbit.

We introduce a new technique for absolute “through-theantenna” calibration of cross-track-scanning passive microwave radiometers viewing earth from a low-earth orbit. This method offers significant advantages, in that neither internal calibration targets nor noise diodes are needed to calibrate the radiometer. The algorithm does require periodic updates of the atmospheric state, which can be readily provided by GPS radio occultation observations, for example. An iterative algorithm retrieves the radiometer gain given a sequence of observations of the earth's limb. The algorithm uses a parameterized radiative transfer model of a spherically-stratified atmosphere. The algorithm works best for opaque temperature sounding channels. This method, when used on idealized radiometer measurements (impulse response functions in frequency and space), yields calibration accuracies similar to those that could be obtained with ideal internal calibration targets. This analysis is based on global Monte Carlo simulations using the NOAA88b profile set. An analysis will also be presented showing how calibration performance degrades as the radiometer characteristics deviate from the ideal case. Among the factors considered are: 1) antenna pattern, 2) spectral passband, 3) pointing errors, 4) atmospheric state variability, 5) the number of limb observations required, and 6) sensitivity to sensor noise.

The Suomi NPP spacecraft pitchover maneuver revealed an ATMS scan angle-dependent bias when viewing deep space, which is a homogenous and unpolarized source that fills the entire ATMS Field of Regard. Reflector emissivity was investigated as a possible root cause. The emissivity is polarization dependent, which results in a scan-dependent bias with the quasi-vertical channels having a different bias shape than the quasi-horizontal channels. The normal emissivity was empirically estimated by minimizing the scan bias during the pitchover maneuver, and the reflector's temperature was derived from ATMS telemetry. The model, calibration change, and estimated normal emissivity were verified using the ATMS thermal vacuum test data. Reflector emissitivies from approximately 0.2% to 0.4% were derived, resulting in brightness temperature corrections of up to 0.5 K.

The 14 papers in this special issue cover a combination of software and hardware solutions to the Radio Frequency Interference (RFI) problem, detail the challenges in monitoring RFI, and attempt to quantify the impact that interference has on measurements.

This forty papers in this special issue focus on how intercalibration and comparison between sensors can provide an effective and convenient means of verifying their postlaunch performance and correcting their measurement differences.

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration.

Neural networks (NNs) are developed for estimating the error variances of individual infrared and microwave atmospheric temperature and humidity profile retrievals, thus potentially significantly improving their assimilation into numerical weather prediction models. Currently, most assimilation processes require error covariance matrices that are typically estimated over diverse profile ensembles. In addition to these “ensemble error variances,” this work explores the estimation of “sample error variances” that are relevant to a single sample of the ensemble (that is, an individual profile retrieval and its error at each pressure level). This analysis is facilitated by considering an individual profile retrieval as the most likely sample from a distribution of retrievals, given an individual sensor observation vector. The sample error variance is defined as the variance of this distribution. The approach described in this paper does not attempt to compute these retrieval distributions explicitly, as this is computationally prohibitive for hyperspectral sounders. Instead, NNs are trained to estimate the variances of these distributions directly. Examples over ocean utilizing AIRS/AMSU soundings on the NASA Aqua satellite and those from a proposed hyperspectral microwave sounder show that the predicted sample error variances agree well with the true sample error variances as determined by European Centre for Medium-Range Weather Forecasts analyzes colocated to the sensor observations. Furthermore, simple quality indicators derived using thresholding of the sample variance estimates compare favorably to AIRS Level-2 Version-5 quality flags.

The Micro-sized Microwave Atmospheric Satellite (MicroMAS) is a 3U cubesat (34×10×10 cm, 4.5 kg) hosting a passive microwave spectrometer operating near the 118.75-GHz oxygen absorption line. The focus of the first MicroMAS mission (hereafter, MicroMAS-1) is to observe convective thunderstorms, tropical cyclones, and hurricanes from a near-equatorial orbit at approximately 500-km altitude. A MicroMAS flight unit is currently being developed in anticipation of a 2014 launch. A parabolic reflector is mechanically rotated as the spacecraft orbits the earth, thus directing a cross-track scanned beam with FWHM beamwidth of 2.4-degrees, yielding an approximately 20-km diameter footprint at nadir incidence from a nominal altitude of 500 km. Radiometric calibration is carried out using observations of cold space, the earth's limb, and an internal noise diode that is weakly coupled through the RF front-end electronics. A key technology feature is the development of an ultra-compact intermediate frequency processor module for channelization, detection, and A-to-D conversion. The antenna system and RF front-end electronics are highly integrated and miniaturized. A MicroMAS-2 mission is currently being planned using a multi-band spectrometer operating near 118 and 183 GHz in a sun-synchronous orbit of approximately 800-km altitude. A HyMAS-1 (Hyperspectral Microwave Atmospheric Satellite) mission with approximately 50 channels near 118 and 183 GHz is also being planned. In this paper, the mission concept of operations will be discussed, the radiometer payload will be described, and the spacecraft subsystems (avionics, power, communications, attitude determination and control, and mechanical structures) will be summarized.

Recent technology advances have profoundly changed the landscape of modern radiometry by enabling miniaturized, low-power, and low-noise radio-frequency receivers operating at frequencies near 200 GHz and beyond. These advances enable the practical use of receiver arrays to multiplex multiple broad frequency bands into many spectral channels. We use the term “hyperspectral microwave” to refer generically to microwave sounding systems with approximately 50 spectral channels or more. In this paper, we report on the design and analysis of the receiver subsystem (lensed antenna, RF front-end electronics, and IF processor module) for the Hyperspectral Microwave Atmospheric Sounder (HyMAS) comprising multiple receivers near the oxygen absorption line at 118.75 GHz and the water vapor absorption line at 183.31 GHz. The hyperspectral microwave receiver system will be integrated into a new scanhead compatible with the NASA GSFC Conical Scanning Microwave Imaging Radiometer/Compact Submillimeter-wave Imaging Radiometer (CoSMIR/CoSSIR) airborne instrument system to facilitate demonstration and performance characterization under funding from the NASA ESTO Advanced Component Technology program. Four identical radiometers will be used to cover 108-119 GHz, and two identical receivers will be used to cover 173-183 GHz. Subharmonic mixers will be driven by frequency-multiplied dielectric resonant oscillators, and single-sideband operation will be achieved by waveguide filtering of the lower sideband. A relatively high IF frequency is chosen to facilitate miniaturization of the IF processor module, which will be fabricated using Low Temperature Co-fired Ceramic (LTCC) technology. Corrugated feed antennas with lenses are used to achieve a FWHM beamwidth of approximately 3.5 degrees. Two polarizations are measured by each feed to increase overall channel count, and multiple options will be considered during the design phase for the polarization diplexing approach. Broadband operation over a relatively high intermediate frequency range (18-29 GHz) is a technical challenge of the front-end receiver systems, and a receiver temperature of approximately 2000-3000K is expected over the receiver bandwidth. This performance, together with approximately 100-msec integration times typical of airborne operation, yields channel NEDTs of approximately 0.35K, which is adequate to demonstrate the hyperspectral microwave concept by comparing profile retrievals with high-fidelity ground truth available either by coincident overpasses of hyperspectral infrared sounders and/or in situ radiosonde/dropsonde measurements.

Atmospheric Vertical Temperature Profile (AVTP) and Atmospheric Vertical Moisture Profile (AVMP) retrievals produced by the Cross-track Infrared Sounder and the Advanced Technology Microwave Sounder suite (CrIMSS) official algorithm were evaluated with global European Center for Medium Range Weather Forecast (ECMWF) analysis fields, radiosonde (RAOB) measurements, and Aqua-Atmospheric Infrared Sounder (AIRS) heritage algorithm retrievals. The operational CrIMSS AVTP and AVMP product statistics with truth data sets are quite comparable to the AIRS heritage algorithm statistics. Planned updates and improvements to the CrIMSS algorithm will alleviate many issues observed with `day-one' focus-day results and show promise in meeting the Key Performance Parameter (KPP) specifications.

As a part of the Joint Polar Satellite System (JPSS) and the Suomi National Polar-orbiting Partnership (NPP), the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS primarily provides globally-referenced calibrated radiances and vertical profiles of temperature, moisture, and pressure. The CrIMSS operational code has been ported to various LINUX systems and retrievals are performed using both proxy and real ATMS/CrIS data. The high quality proxy data generated from the IASI instrument provided useful testing for the CrIMSS EDR algorithm prior to the launch of the SUOMI NPP satellite. The experience learned from processing the proxy data helped us to handle the SUOMI NPP CrIS/ATMS data as soon as they became available to the CAL/VAL team. In this paper, encouraging preliminary results of applying the ported CrIMSS EDR algorithm to the SUOMI NPP CrIS/ATMS data are presented.

This paper develops a precipitation retrieval algorithm for the Advanced Technology Microwave Sounder (ATMS) recently launched aboard the U.S. Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The algorithm is called the ATMS MIT Precipitation retrieval algorithm version 1 (ATMP-1), employs neural network estimators trained and evaluated using the validated global reference physical model NCEP/MM5/TBSCAT/F(λ), and works for snow-free land and seawater with |latitudes|<;50°. Signals were carefully chosen and principal component analysis was used to filter out angle and surface effects, and other noises. Retrievals are useful for surface precipitation rates higher than 1 mm/h at 15-km resolution for both land and sea, as evaluated using MM5. Surface precipitation rates retrieved using ATMP-1 for ATMS aboard Suomi NPP satellite are in good agreement with those retrieved using the AMSU MIT Precipitation retrieval algorithm (AMP) for AMSU aboard NOAA-18 satellite.

A suite of sensors scheduled to fly onboard the NPOESS Preparatory Project (NPP) satellite in 2011 will continue the Sensor Data Records (SDRs) provided by operational and research missions over the last 40 years. The Cross-track Infrared and Microwave Sounding Suite (CrIMSS), consisting of the Cross-track Infrared Sounder (CrIS) and the first space-based, Nyquist-sampled cross-track microwave sounder, the Advanced Technology Microwave Sounder (ATMS), will provide atmospheric vertical profile information to improve numerical weather and climate modeling. The ability of ATMS to sense temperature and moisture profile information in the presence of non-precipitating clouds complements the high vertical resolution of CrIS. Furthermore, the ability of ATMS to sense scattering of cold cosmic background radiance from the tops of precipitating clouds allows the retrieval of precipitation intensities with useful accuracies over most surface conditions. This paper will present several assessments of the performance of ATMS. Prelaunch testing of ATMS has characterized the principal calibration parameters and has enabled predictions of on-orbit performance with high levels of confidence. Also to be discussed is the planned on-orbit characterization of ATMS, which will further improve both the measurement quality and the understanding of various error contributions.

Neural networks are developed for estimating the rms accuracy profiles of individual infrared and microwave atmospheric temperature and humidity profile retrievals, thus potentially significantly improving their assimilation into numerical weather prediction models. Currently most assimilation processes compute retrieval variances or error-covariance matrices as ensemble averages over diverse profiles, or simply flag problematic retrievals. Although retrieval accuracies vary considerable from profile to profile because of clouds, even in cloud-free cases they can differ markedly. The ability to estimate accurately the variances of individual profiles is one of the benefits of hyperspectral infrared and microwave sounding. The variance-estimating neural network was trained to estimate the logarithm of variance, which was then mapped to standard deviation. Examples utilizing AIRS/AMSU/HSB soundings [1] on the NASA Aqua satellite and those from a proposed hyperspectral microwave sounder [2], [3] show that when the predicted rms errors for a single altitude are stratified, they agree with the actual rms errors within perhaps ten percent of the dynamic range of the stratifications thus significantly improving the potential for accurately weighting soundings against model parameters during assimilation. Simple quality indicators using the new variance estimates also favorably compare to AIRS Level 2 Version 5 quality flags.

Radiometric digital count data are required to test the operational software of the Advanced Technology Microwave Sounder (ATMS) prior to its launch in 2011 to verify effective calibration procedures and optimize performance. These data, however, do not exist prior to launch, thus necessitating a method to build a model of the ATMS that synthesizes realistic data as though the sensor were space-borne. A method to build such a model is described in this paper. The procedure and application are specific to the ATMS but could be applied to other passive microwave radiometers as well.

We introduce a new hyperspectral microwave remote sensing modality for atmospheric sounding, driven by recent advances in microwave device technology that now permit receiver arrays that can multiplex multiple broad frequency bands into more than 100 spectral channels, thus improving both the vertical and horizontal resolutions of the retrieved atmospheric profile. Global simulation studies over ocean and land in clear and cloudy atmospheres using three different atmospheric profile databases are presented that assess the temperature, moisture, and precipitation sounding capability of several notional hyperspectral systems with channels sampled near the 50-60-, 118.75-, and 183.31-GHz absorption lines. These analyses demonstrate that hyperspectral microwave operation using frequency multiplexing techniques substantially improves temperature and moisture profiling accuracy, particularly in atmospheres that challenge conventional nonhyperspectral microwave sounding systems because of high water vapor and cloud liquid water content. Retrieval performance studies are also included that compare hyperspectral microwave sounding performance to conventional microwave and hyperspectral infrared approaches, both in a geostationary and a low-Earth-orbit context, and a path forward to a new generation of high-performance all-weather sounding is discussed.

We introduce a new hyperspectral microwave remote sensing modality for atmospheric sounding, driven by recent advances in microwave device technology that now permit receiver arrays that can multiplex multiple broad frequency bands into more than ~ 100 spectral channels, thus improving both the vertical and horizontal resolution of the retrieved atmospheric profile. Global simulation studies over ocean and land in clear and cloudy atmospheres using three different atmospheric profile databases are presented that assess the temperature, moisture, and precipitation sounding capability of several notional hyperspectral systems with channels sampled near the 50-60-GHz, 118.75-GHz, and 183.31-GHz absorption lines. These analyses demonstrate that hyperspectral microwave operation using frequency multiplexing techniques substantially improves temperature and moisture profiling accuracy, especially in atmospheres that challenge conventional non-hyperspectral microwave sounding systems because of high water vapor and cloud liquid water content. Retrieval performance studies are also included that compare hyperspectral microwave sounding performance to conventional microwave and hyperspectral infrared approaches, both in a geostationary and low-earth orbit context, and a path forward to a new generation of high-performance all-weather sounding is discussed.

As a part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and the NPOESS Preparatory Project (NPP), the instruments Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure profiles and calibrated radiances [1]. In preparation for the NPOESS/NPP launch, porting and testing of the CrIMSS Environmental Data Record (EDR) algorithms need to be performed.

Spatially oversampled radiometric measurements of the Earth's surface and atmosphere can be reprocessed to optimize various performance metrics. These metrics include resolution, signal-to-noise ratio, and accuracy of retrieved environmental data. In this study, we explore several strategies for two-dimensional image processing for microwave sounder observations and assess the performance using several metrics. Simulated measurements of the Advanced Technology Microwave Sounder (ATMS), the Microwave Imager/Sounder (MIS), and the Geostationary Microwave Array Spectrometer (GeoMAS) are used in the evaluation. We examine spatial filtering (a modification of the effective spatial resolution of the measurements), and in the ATMS case, resampling (a modification of the effective boresight of the composite footprint).

A novel statistical method for the retrieval of atmospheric temperature and water vapor profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETSAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of the stochastic cloud clearing methodology together with neural network estimation. A stand-alone statistical algorithm will be presented that operates directly on cloud-impacted AIRS/AMSU and IASI/AMSU data, with no need for a physical cloud clearing process. The performance of this method was evaluated using global (ascending and descending) EOS-Aqua orbits collocated with ECMWF fields for a variety of days throughout 2003, 2004, 2005, and 2006. Over 1,000,000 fields of regard (3×3 arrays of footprints) over ocean and land were used in the study. The method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance. Retrieval accuracy will be evaluated using ECMWF atmospheric fields as ground truth. The accuracy of the neural network retrieval method will be compared to the accuracy of the AIRS Level 2 (Version 5) retrieval method.

This paper outlines the results of two recent efforts to use the NPOESS Aircraft Sounder Testbed-Microwave (NAST-M) airborne sensor to directly validate the microwave radiometers on a number of operational satellites. Radiance differences between the NAST-M sensor and the Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sensor (MHS) were found to be less than 1 K for most channels. Comparison results for ocean underflights of the Aqua, NOAA, and MetOp-A satellites are shown.

This manuscript focuses on recent efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (National Polar-orbiting Operational Environmental Satellite System) satellite program. Emphasis will be placed on the following three critical components: a methodology for simulating passive microwave observations, a technique for validating the methodology with aircraft measurements, and a statistics-based algorithm for estimating precipitation rate.

A nonlinear stochastic method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU), and is presently being adapted for use with the NPOESS Cross-track Infrared Microwave Sounding Suite (CrIMSS) consisting of the hyperspectral Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). The algorithm is implemented in three sequential stages: 1) stochastic cloud clearing (SCC), 2) eigenvector radiance compression and denoising, and 3) neural network (NN) estimation. First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing of the infrared and microwave data. Second, a Projected Principal Components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, a feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. The performance of the algorithm (henceforth referred to as SCC/NN) was evaluated using global (ascending and descending) EOS-Aqua orbits co-located with ECMWF forecasts (generated every three hours on a 0.5-degree lat/lon grid) and radiosonde observations (RAOBs) for a variety of days throughout 2003 and 2004. Over 500,000 fields of regard (3times3 arrays of footprints) over ocean and land were used in the study. The performance of the SCC/NN algorithm exceeded that of the AIRS Level 2 (Version 4) algorithm throughout most of the troposphere while achieving approximately four times the yield. Furthermore, the SCC/NN performance in the lowest 1 km of the atmosphere greatly exceeds that of the AIRS Level 2 algorithm as the level of cloudiness increases. The SCC/NN algorithm requires significantly less computation than traditional variational retrieval methods while achieving comparable performance, thus the algorithm is particularly suitable for quick-look retrieval generation for post-launch CrIMSS performance validation.

This paper will present efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (national polar-orbiting operational environmental satellite system) satellite program and the NPOESS preparatory project (NPP) prior to the launch of the first satellite in 2009. The advanced technology microwave sounder (ATMS) offers improvements including finer sampling and spatial resolution over heritage instruments such as the advanced microwave sounding unit instruments AMSU-A/B aboard the NOAA-15, NOAA-16, and NOAA-17, and similar instruments. The conical scanning microwave sounder (CSMS) is planned for the second and subsequent NPOESS satellites. A system for simulating ATMS and CSMS microwave observations from atmospheric data has been developed. This system has shown encouraging results when validated with observations from AMSU-B on NOAA-16. This system is flexible and can be used not only with cross-track scanning instruments but also with conically scanning instruments. A neural network was trained to estimate 5.2deg MM5 rain rates from simulated ATMS observations. Encouraging agreement was observed. However, this algorithm is only preliminary and many improvements are in progress.

This paper reviews recent progress in neural-network-based atmospheric parameter retrieval algorithms. Satellite-based observations of the atmosphere are related to atmospheric and surface variables by the radiative transfer equation. Direct inversion of the radiative transfer equation is often intractable as the underlying atmospheric phenomenology is usually neither linearly related to the observations nor Gaussian. Neural networks provide a computationally efficient alternative with their relatively simple structure. However, effective use of neural network techniques in problems of this nature requires careful preprocessing of the input data, and this preprocessing is often highly dependent on the remote sensing scenario under consideration. In this paper, we present techniques for preprocessing neural network input data in the spectral, spatial, and temporal domains. The utility of these techniques is demonstrated with several applications, including the estimation of the atmospheric temperature profile, water vapor profile, and precipitation rate using satellite-based passive microwave and infrared measurements.

A novel statistical method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with simulated clear-air and observed partially cloudy sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). The algorithm is implemented in two stages. First, a projected principal components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Second, a multilayer feedforward neural network (NN) is used to estimate the desired geophysical parameters from the PPCs. For the first time, NN temperature and moisture retrievals are presented using actual microwave and hyperspectral infrared observations of cloudy atmospheres, over both ocean and land (with variable terrain elevation), and at all sensor scan angles. The performance of the NN retrieval method (henceforth referred to as the PPC/NN method) was evaluated using global Earth Observing System Aqua orbits colocated with European Center for Medium-range Weather Forecasting fields for seven days throughout 2002 and 2003. Over 350,000 partially cloudy footprints were used in the study, and retrieval performance was compared with the AIRS Science Team Level-2 retrieval algorithm (version 3). Performance compares favorably with that obtained with simulated clear-air observations from the NOAA88b radiosonde set of approximately 7500 profiles. The PPC/NN method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance.

A novel statistical method for the retrieval of atmospheric temperature and moisture (relative humidity) profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). The algorithm is implemented in three stages. First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing of the infrared and microwave data. Second, a Projected Principal Components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, an artificial feedforward neural network (NN) is used to estimate the desired geophysical parameters from the projected principal components. The cloud-clearing of the infrared radiances was performed by the AIRS Science Team using infrared brightness temperature contrasts in adjacent fields of view and microwave-derived estimates of the infrared clear-column radiances to estimate and correct the radiance contamination introduced by clouds. The PPC compression technique was used to reduce the infrared radiance dimensionality by a factor of ~100, while retaining over 99.99 percent of the radiance variance that is correlated to the geophysical profiles. This compression allows the use of smaller, faster, and more robust estimators. A single-layer feedforward neural network with approximately 3000 degrees of freedom was then used to estimate the geophysical profiles in 1-km layers from the surface to 20 km. The performance of this method (henceforth referred to as the PPC/NN method) was evaluated using global EOS-Aqua orbits colocated with European Center for Medium-range Weather Forecasting (ECMWF) fields for two days in 2003: September 3 and October 12. Over 15,000 footprints over ocean were used in the study. Retrieval performance compares favorably with that obtained with simulated observations from the NOAA88b radiosonde set of approximately 7500 profiles. The PPC/NN method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance

A novel statistical method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with simulated clear-air hyperspectral sounding data. The algorithm is imple- mented in two stages. First, a Projected Principal Components (PPC) trans- form is used to reduce the dimensionality of and optimally extract geophys- ical profile information from the spectral radiance data. Second, a multi- layer feedforward neural network (NN) is used to estimate the desired geo- physical parameters from the projected principal components. The perfor- mance of this method (henceforth referred to as the PPC/NN method) was evaluated using simulated clear-air observations from the 2378-channel At- mospheric Infrared Sounder (AIRS). One particularly noteworthy result of the present work is a comparative study between the PPC/NN method and an iterated minimum-variance (IMV) method. The retrieval performance of the PPC/NN algorithm exceeds that of the IMV method over most of the atmosphere while requiring less computation. The retrieval performance of both the PPC/NN and IMV methods is significantly better than that of linear regression.

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Aircraft Sounder Testbed-Microwave, or NAST-M , has added two spectrometers to its original suite of spectrometers operating near the oxygen lines at 50-57 GHz and the oxygen line at 118.75 GHz. The NAST-M suite now includes a spectrometer that is centered on the water vapor absorption line at 183.31 GHz, and another that is centered on the oxygen absorption line at 424.76 GHz. This addition has increases the ability of NAST-M to retrieve humidity profiles and to sense smaller-diameter hydrometeors. All four of the spectrometers' horns are collocated, have 3-dB (full-width at half-max) points of 7.5/spl deg/, and are directed at a single scanning mirror, which produces a cross-track scan beneath the aircraft with a swath width of approximately 100 km. Aircraft carrying NAST-M cruise at altitudes of 17 to 20 km, yielding a nadir surface spatial resolution of /spl sim/ 2.6 km.

Data collected by airborne infrared (NAST-I, MAMS) and microwave (NAST-M) sensors during CAMEX-3 (Florida, summer 1998) are used to quantify the cloud impact on atmospheric sounding performance in several ways. First, the degrees of freedom introduced by clouds are estimated by applying principal component transforms to cloud-perturbed (clear minus cloudy) NAST spectra. These results are then compared to those obtained using radiances simulated using two-level clouds. Finally, the radiance cloud-clearing performance for NAST-type clouds is evaluated and compared with results for simulated two-level clouds.

The temperature profile retrieval performance of airborne and spaceborne atmospheric infrared sounders is significantly degraded by clouds. The impact due to two-level clouds is quantitatively assessed in two ways using simulated radiances from the Atmospheric InfraRed Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Microwave Humidity Sounder (MHS). First, cloud characterization (cloud flagging) techniques were developed to identify cloud-contaminated pixels. Second, a neural network was trained to estimate clear-column infrared radiances from cloud-contaminated 3/spl times/3 pixel clusters of infrared and microwave radiances (cloud clearing). This algorithm reduced the a priori radiance error due to clouds by at least a factor of three for those 4- and 15-micron sounding channels with weighting function peaks located from 0-10 km. Radiance cloud-clearing errors (RMS) for infrared channels with weighting functions peaking from 10 to 0 km ranged from 0.1 to 0.7 K for the 4-micron channels and from 0.1 to 1.2 K for the 15-micron channels. Combining microwave and infrared data at the pixel level always yielded results superior to infrared alone. It was found that microwave surface emissivity uncertainties are a key contributor to residual errors.

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Aircraft Sounder Testbed (NAST) has been developed and deployed on the NASA ER-2 high-altitude aircraft. The testbed consists of two co-located cross-track scanning instruments: a Fourier transform interferometer spectrometer (NAST-I) with spectral coverage of 3.7-15.5 /spl mu/m and a passive microwave spectrometer (NAST-M) with 17 channels near the oxygen absorption lines at 50-57 GHz and 118.75 GHz. The testbed provides the first coregistered imagery from high-resolution microwave and infrared sounders and will provide new data that will help (1) validate meteorological satellite environmental data record feasibility, (2) define future satellite instrument specifications, and (3) demonstrate operational issues in ground validation, data calibration, and retrievals of meteorological parameters. To help validate the performance and potential of NAST-M, imagery was collected from more than 20 overpasses of hurricanes Bonnie and Earl during the Convection and Moisture Experiment (CAMEX-3), Florida, boreal summer 1998. The warm core and convection morphology of Hurricane Bonnie (August, 1998) is clearly revealed both by aircraft-based microwave brightness temperature imagery and temperature retrievals within the eye. Radiance comparisons with the Advanced Microwave Sounding Unit on the NOAA-15 satellite and radiosonde observations yield root mean-squared agreements of approximately 1 K or less.