Real-time precipitation retrieval is crucial for timely warnings of extreme weather events such as heavy rain or floods. Geostationary satellite observations combined with machine learning methods provide an effective way to achieve real-time precipitation retrieval, yet are hampered by two issues. The traditional stable model, pre-trained with historical data, lacks precipitation information from adjacent time periods and its accuracy declines over time. Geostationary satellites can only capture cloud top information, leading to inaccurate localization of precipitation areas in estimations based solely on such data. To solve these problems, a novel Dynamic Two-step Real-time Precipitation Retrieval Algorithm (D-PRA) has been developed. It conducts real-time retrieval in two steps through dynamic data and variables selection: identifying precipitation and then retrieving precipitation intensity. It further incorporates atmospheric profile information to enhance the details of in-cloud and under-cloud conditions. D-PRA was applied to Himwari-8 observations and was validated with the rain gauge observations in Chinese regions at the hourly scale. The results showed that, in comparison with GSMaP_NOW, D-PRA exhibits significant enhancements. The probability of detection attained 0.72, representing a nearly fivefold increment, and the root mean square error was 0.99 mm, a 25.5% reduction. Moreover, D-PRA is stable across seasons and time periods, demonstrating its good reliability and robustness. D-PRA has great potential to improve the accuracy of precipitation retrieval for extreme event monitoring and disaster management.

Near real-time precipitation estimation from geostationary satellites plays an important role in flood forecasting, water resource management, and disaster prevention and reduction. Currently, near real-time precipitation products based on geostationary satellites still face great challenges in accurately detecting precipitation and monitoring small-scale precipitation. In this study, a novel Geostationary Satellite Precipitation Estimation (GSPE) algorithm for near real-time condition was developed to retrieve precipitation at a spatial resolution of 0.05°×0.05° every 10 minutes both day and night. The major highlight of the GSPE is that a new precipitation detection scheme was created by introducing a newly proposed precipitation detection index (PDI) and 24-hour continuous cloud microphysical parameters for the first time. Another highlight is that a dynamic updating scheme was proposed in building conversion models between brightness temperature of geostationary satellite and precipitation to keep the accuracy and stability of the estimated precipitation. Furthermore, the 10-minute temporal resolution of the estimated precipitation could accurately capture the evolution of a short precipitation process and improve the calculation of total precipitation amount. According to the validation using rain gauges observations from Chinese mainland, the Heidke Skill Score of the GSPE in hourly scale could reach up to 0.39 which was improved by 11.43% compared to the GSMaP_NOW. The root mean square error of precipitation from the GSPE in hourly, daily, and monthly scale are 1.66mm, 13.65mm, and 97.76mm respectively, and were improved by 15.74%, 13.17%, and 21.01% respectively compared to that of the GSMaP_NOW.

1 Abstract-The cloud water path (CWP) has an important influence on the radiative effects of clouds and the water cycle in the Earth’s atmospheric system, serving as a key parameter in physical cloud processes. In this study, a novel method for retrieving CWP by leveraging the advantages of multisource and multiband active and passive satellite observations is proposed. A retrieval model to retrieve CWP that using Himawari-8/AHI) thermal infrared channels is established by learning from active radar (CloudSat) measurements, the model enables continuous CWP retrieval throughout the day. Compared with all-day CloudSat-CWP, our CWP products has has a higher retrieval accuracy that that of MODIS. The distribution of the monthly average CWP product based on the Himawari-8 full-disk dataset resembles that of CloudSat observations, with the highest average CWPs in equatorial region, followed by the CWPs in midlatitude regions. This spatial pattern of CWP is possibly due to the prevalence of strong convective systems in these areas, which facilitate the formation and progression of deep clouds, leading to higher CWP values. This algorithm can offer valuable data support for atmospheric-related analyses and has been integrated into the Cloud Remote Sensing, Atmospheric Radiation, and Renewable Energy Application (CARE) platform for atmospheric remote sensing algorithms.

Accurate and timely precipitation data plays a crucial role in flood monitoring, forecasting and emergency management. The primary challenge with current real-time precipitation products is their limited accuracy in identifying precipitation. To improve the accuracy of precipitation retrieval, a dynamic two-step precipitation retrieval method was developed, i.e., first identifying precipitation and then estimating its intensity. The geostationary satellite infrared brightness temperature data, atmospheric profile data, and rain gauge observations were used to construct the precipitation identification model and precipitation intensity retrieval model based on a random forest machine learning method. This method was applied to precipitation retrieval in China and surrounding areas using the Himawari-8 observations and the ERA5 atmospheric profile data. The results of hourly precipitation identification showed good agreement with rain gauge observations, with a Probability of Detection of 0.725 and a False Alarm Ratio of 0.426. The estimated hourly precipitation intensity agrees well with the rain gauge data and shows a lower error than the GSMaP_NOW real-time precipitation product. The good performance of the developed method in this study for real-time precipitation monitoring was also confirmed in different seasons.

Tropical monsoon climate in IndoChina Peninsula features dry and rainy season. Microwave remote sensing offers emerging capabilities for hydrological simulation. This paper aims to clarify whether the contributions of remote sensing precipitation on runoff simulation will change due to terrains and model algorithms. We simulated runoff in mountainous area-Yuan River Basin based on remote sensing early version precipitation products by using Soil & Water Assessment Tool (SWAT) model. We also simulated runoff in flat terrain area-Mun-chi River Basin based on remote sensing final version precipitation products by Variable Infiltration Capacity Model (VIC) model. We compared the runoff results against gauge-based CMORPH-AWS and World Meteorological Organization (WMO) interpolated precipitation, and also estimated flood inundation areas in Mun-chi River from 2005 to 2014 based on runoff simulations. The results show that (1) gauge-based precipitation products CMORPH-AWS and WMO precipitation have largest NSE and smallest RMSE for runoff simulation in these two basins. The runoff simulation by VIC model and SWAT model based on TRMM Multi-satellite Preciptiation Analysis (TMPA) remote sensing product have higher correlation with the observations; (2) Runoff simulations based on TMPA can be used for flood inundation area estimation in larger river basin rather than smaller basin or subbasin. Our study reveals that high-quality precipitation products significantly improved runoff simulation accuracy in these two basins. Remote sensing precipitation product TMPA has potential on runoff simulation and flood assessment in remote or observation lacking area in IndoChina Peninsula.

In this study, a quantitative Freeze/thaw (F/T) index from passive microwave observations is defined, and is assumed to be linearly correlated with land surface temperature from thermal infrared observations. Thus, a linear regression method is proposed and verified to be effective over a multiscale network of Naqu of the Tibetan Plateau. Then, we implement and test the proposed approach to generate daily F/T state maps at a 5-km spatial resolution through the fusion of AMSR2 and MODIS data. It is found the high resolution F/T maps agreed well with ground reference observations of 0-cm soil temperature, with an overall accuracy of ~86.6%. This study provides new insights for high-resolution F/T mapping beyond the (Soil Moisture Active Passive) SMAP mission.

Atmospheric water vapor is a key parameter in the study of global water cycle and climate change. As water vapor is also an important kind of greenhouse gas, knowledge of decades of total precipitable water (TPW) is very important in understanding the effect of atmospheric water vapor on global water cycle and climate change. In this study, a decade of daily and monthly TPW product with a spatial resolution of 0.25°×0.25° is produced in all-weather condition based on the combination of TPW from MODIS in clear sky condition, TPW retrieved from AMSR-E in all-weather condition over land, and TPW derived from AMSR-E L2 Ocean product in all-weather. As a validation source, TPW obtained from globally distributed SuomiNet GPS network will be used to validate the accuracy of the ten years TPW product in daily and monthly scale.

Land surface longwave (LW) radiation (or longwave radiative flux), including longwave upwelling (LWUR), downward (LWDR) and net radiation (LWNR), are key components of the total energy that drives the surface energy balance at the interface between the earth's surface and the atmosphere. The importance of LW radiation in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a tremendous attempts have been made to derive LW radiation from space only valid under clear-sky conditions leading to difficulty of utility of remote sensing-based LW radiation products in most land models due to their spatial discontinuity. Although few studies focused on LW radiation estimation under cloudy-sky conditions, while their global application are still problematic. In this paper, novel strategies are proposed aiming to derive high resolution cloudy-sky LWDR and LWUR by fusing collocated optical and microwave satellite data. The results reveal that the new approaches work rather well, thus, more importantly, providing unprecedented possibilities for generating high resolution global LW radiation datasets.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies. The WCOM is expected to be implemented during the 13thfive-year-plan period (2016–2020).

Snow water equivalent is a key parameter in hydrology and climatology. In this study, we estimates snow water equivalent based on bi-continuous vector radiative transfer (VRT) model at X (9.6 GHz) and Ku (17.2 GHz) bands radar scatter. First, the relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from bi-continuous VRT model. Then, cost function with constraints is used to solve effective albedo and optical thickness and absorption part of optical depth can be obtained from these two parameters. The backscattering signals before snowfall are regarded as ground backscattering signals under snow cover. We finally retrieve snow water equivalent from backscattering signals with X and Ku bands at VV and VH polarizations. The retrieval algorithm is validated utilizing ground measurements from NoSREx (Nordic Snow Radar Experiment) campaign.

Water vapor plays an important roles in the Earth's energy and water cycles. Compared to optical remote sensing, microwave remote sensing has the advantage to acquire information of atmosphere under cloudy condition. Up to now, there is no published reliable total precipitable water product over land from AMSR2 due to effect of high land surface emissivity in microwave band. In this study, an improved total precipitable water retrieved algorithm for AMSR2 will be developed based on previous studies. In the retrieval algorithm, a land surface emissivity parameter estimation model is developed using combination of AMSR2 and MODIS observation. The precisely estimated surface emissivity parameter is the key parameter in the retrieval of total precipitable water. Finally, the total precipitable water was retrieved using a look-up table, and it is validated using total precipitable water observed from global distributed GPS.

In this study, a novel approach is proposed to simultaneously detect clouds and cloud shadows for remotely sensed images. Unlike the existing methods that based on spectral tests, it is based on the simulated band radiance, so that it can be applied to any remotely sensed images. The results showed that it very effective compared to existing algorithms.

Frozen ground is soil or rock in which part or all of the pore water has turned into ice. Freeze/thaw state is simply water-ice phase change, but it is an important sign like a giant on-off “switch” of the land surface processes. The freezing of soil greatly reduces the water infiltration and migration in the soil, and in consequence generates a substantial increase in snowmelt runoff. The seasonal cycles of freezing and thawing significantly influence the surface energy exchanges with atmosphere. Therefore, freeze/thaw state monitoring is becoming essential under the context of global changes. The WCOM integrates all the advantages of previous satellites, and is expected to provide more accurate information of freeze/thaw state through the synergy use of active and passive, high and low resolution measurements.

Estimate surface air temperature (Ta) accurately in fine scale is very necessary for hydrological simulation, especial in glacierized basins. The purpose of this paper is to present a framework to mapping the Ta using multi-source remote sensing data and reanalysis dataset. The main content includes two parts: (a) filling the gaps in remotely sensed land surface temperature (LST) using spatial-temporal Kriging method and (b) developing a semi-empirical method to relate Ta and LST that is applicable in glacierized basins. The framework is further tested in the Langtang valley, Nepal which is a glacierized basin in the central Hindu-Kush-Himalaya (HKH) region. The validation results show that the estimated Ta has generally good spatial and temporal variations. The RMSE of Ta at Langtang Kyangjin station is 9.1K and 7.7K at 10:30 and 13:30, respectly.

L-band radiometry is a promising pathway for soil moisture estimation at global scale. An observation system simulation experiment was conducted for LEWIS over the SMOSREX bare soil site in 2006 through coupling the Variable Infiltration Capacity(VIC) land surface model and a Multi-Option L-band Microwave Emission Model(MOLMEM) in this study. Impacts from different dielectric constant models and roughness correction schemes on brightness temperature simulation were analyzed.

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

Accurate microwave brightness temperature is important for the retrieval of land surface parameter. However, the existence of atmosphere affect acquisition of brightness temperature by microwave sensor onboard satellite. In this paper, atmosphere sensitivity of each band of AMSR-E is analyzed and an atmosphere correction method is developed with ancillary water vapor and cloud liquid water data for both clear and cloudy condition. As a validation, time series of microwave vegetation index is used to qualitatively verify the atmosphere corrected brightness temperature, and it shows that the atmosphere correction method make a good improvement on microwave vegetation index.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies.

Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.

Water vapor under cloud cover area was retrieved with the combination of AMSR-E Brightness temperature and MODIS atmospheric profile. In order to retrieve water vapor, surface emissivity in clear sky was first estimated using AMSR-E brightness temperature, MODIS atmospheric profiles product and 1-Dimension Microwave Radiative Transfer Model (1DMRTM). And then, surface emissivity under cloud cover area was estimated using 7 days average of that in clear sky. Finally, water vapor was retrieved using the estimated emissivity, AMSR-E brightness and look up table built by 1DMRTM. The finally retrieved water vapor was verified using SuomiNet GPS water vapor. The correlation coefficient of the two is 0.72, and the RMSE is 10.95mm.

The resolution of current MODIS aerosol optical thickness (AOT) product is 10 km. This product is suitable for global research, but it faces difficulty in local area research, especially in a city. In order to get detail aerosol distribution in local area or a city, this article mainly discussed how to retrieve 1 km resolution AOT and how to estimate surface reflectance in the visible from archived MODIS surface reflectance product. The archived MODIS surface reflectance product is mainly used to build surface reflectance database that is used to estimate surface reflectance in the visible. Based on the database, the surface reflectance of band blue was first estimated and then the AOT of Beijing was retrieved using Dense Dark Vegetation (DDV) method and the surface reflectance estimated using the database.