The Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) is a satellite mission jointly developed by the Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA). One challenging feature of this mission is the observation of Doppler velocity by the Cloud Profiling Radar (EC-CPR). The Doppler measurement accuracy is affected by random errors induced by Doppler broadening due to the finite beamwidth and Doppler folding caused by the finite pulse repetition frequency. We investigated the impact of horizontal (along-track) integration and unfolding methods on the reduction of Doppler errors, in order to improve Doppler data processing in the JAXA standard algorithm. We simulated EC-CPR-observed Doppler velocities from pulse-pair covariances with the latest EC-CPR specifications using the radar reflectivity factor and Doppler velocity fields simulated by a satellite data simulator and a global cloud system resolving simulation. Two representative cases of a cirrus cloud and precipitation were examined. In the cirrus cloud case, the standard deviation of random error was decreased to 0.5 m/s for −10 dB${Z} _{\mathbf {e}}$after 10-km horizontal integration. In the precipitation case, large falling speeds of precipitation caused Doppler folding errors due to larger Doppler velocities than that in the cirrus cloud case. When${Z} _{\mathbf {e}}$is larger than −15 dB${Z} _{\mathbf {e}}$, the standard deviations of random error were less than 1.0 m/s after 10-km horizontal integration and unfolding.

This paper describes a cloud liquid water (CLW) database derived from a global cloud-system resolving model NICAM for precipitation retrievals of Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory. Furthermore, impacts of the CLW assumptions were evaluated by experiments using the operational DPR Level-2 algorithm that provides estimated precipitation rates. The CLW database was calculated using the 3.5km-mesh global atmospheric simulation data as a function of surface precipitation rate (SPR), precipitation type (convective or stratiform), temperature, latitude, and land surface type. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages. The CLW database was calculated using the 3.5km-mesh global atmospheric simulation data as a function of surface precipitation rate (SPR), precipitation type (convective or stratiform), temperature, latitude, and land surface type. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages.

Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was $\sim$4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.

Wind is fundamental in many atmospheric phenomena. Global wind profile observation is important to improve numerical weather prediction (NWP) and various meteorological studies. Wind profile observations are measured mainly by radiosonde networks. Most of the weather stations are on land, while weather stations on oceans and remote land areas are sparsely distributed. The current global wind observing systems do not satisfy the globally homogeneous wind profile observation. A space-based Doppler wind lidar is one of the candidates for future global wind profile observations. In the paper, we present results from feasibility study of space-based Doppler Wind Lidar.

In this paper, we develop a rank-mapping algorithm for an icosahedral grid system on a massive parallel computer with the 3-D torus network topology, specifically on the K computer. Our aim is to improve the weak scaling performance of the point-to-point communications for exchanging grid-point values between adjacent grid regions on a sphere. We formulate a new rank-mapping algorithm to reduce the maximum number of hops for the point-to-point communications. We evaluate both the new algorithm and the standard ones on the K computer, using the communication kernel of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), a global atmospheric model with an icosahedral grid system. We confirm that, unlike the standard algorithms, the new one achieves almost perfect performance in the weak scaling on the K computer, even for 10,240 nodes. Results of additional experiments imply that the high scalability of the new rank-mapping algorithm on the K computer is achieved by reducing network congestion in the links between adjacent nodes.

The JAXA-NASA Joint Algorithm Team has developed the Level 2 algorithm for Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory, which will be launched in 2014. Correction method for attenuation by cloud liquid water (CLW) is one of future issues in the DPR algorithm. Recently, a 3.5km-mesh global simulation has been performed using a Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The NICAM is a global cloud-system resolving model (GCRM), and explicitly calculates moist convection using a cloud microphysical scheme. In this work, vertical profiles of the CLW are classified with reference to temperatures and intensities of surface rain rate (SRR) using the NICAM data. Overall, the CLW amount tends to increase when the SRR increases. In the Tropics such as 15S-15N, clear peaks are found around 10-15 degrees Centigrade with small SRRs. This can be connected with shallow rainfall. In large SRRs, peaks are around the Freezing Level, which can be related to tall rainfall.

The synthetic aperture radar (SAR) has been utilized for monitoring natural disaster areas because the SAR observation can be performed irrespective of the daylight and weather conditions. To reduce the time from observation to distributing SAR images to the headquarters for disaster control and disaster area, we have developed an onboard processor which is capable of doing a full-spec processing for the airborne SAR called as the polarimetric and interferometric SAR (Pi-SAR2) since 2009. We demonstrated that the developed onboard processor is effective to reduce the time from observation to distribution and the SAR image can be transferred to the ground within 15 min after observation via the communication satellite called as the engineering test satellite VIII (ETS-VIII) in the experiments on March 2011.

Helicopter videoing is one of the most popular methods to acquire on scene information for disaster management and relief. We developed an on scene disaster information transmission system using Ku-band helicopter satellite communications. The main obstacle for a helicopter satellite communication link is the interruption by the rotating blades of the helicopter. To overcome the blade interruption, we use a time diversity transmission technique at the forward link and a blade-synchronized transmission technique at the return link. Furthermore, onboard position estimation is done for the on scene videoing pictures. Some primary field experiments have been done with satisfactory results.

A real time system for disaster information collection and transmission using ka-band aeronautical satellite communications has been developed. This system is composed of three subsystems: a Ka-band aeronautical satellite communication (KASC) subsystem, a position-estimation and data-processing (PEDP) subsystem, and an information-distributing Internet application subsystem. Experiments to examine the performance of the overall system have been carried out. In this paper, we'll outline the overall experimental system and present some experiment results.