Constellations of small satellites equipped with synthetic aperture radar (SAR) payloads can realize observations in short time intervals independently from daylight and weather conditions and this technology is now in the early stages of development. This tool would greatly contribute to rapid flood monitoring, which is usually one of the main missions in upcoming plans, but few studies have focused on this potential application and a required observation performance for flood disaster monitoring has been unclear. In this study, we propose an unprecedented method for investigating how flood extents would be temporally and spatially observed with a SAR small-satellite constellation and for evaluating that observation performance via an original index. The virtual experiments of flood monitoring with designed constellations were conducted using two case studies of flood events in Japan. Experimental results showed that a SAR small-satellite constellation with sun-synchronous orbit at 570 km altitude, 30-km swath, 15–30° incidence angle, and 20 satellites can achieve 87% acquisition of cumulative flood extent in total observations. There is a difference between the results of observation performance in two cases because of each flood’s characteristics and a SAR satellite’s observation system, which implies the necessity of individual assessments for various types of rivers.

To optimize flood management, it is crucial to determine whether rain will fall within a river basin. This requires very fine precision in prediction of rainfall areas. Cloud data assimilation has great potential to improve the prediction of precipitation area because it can directly obtain information on locations of rain systems. Clouds can be observed globally by satellite-based microwave remote sensing. Microwave observation also includes information of latent heat and water vapor associated with cloud amount, which enables the assimilation of not only cloud itself but also the cloud-affected atmosphere. However, it is difficult to observe clouds over land using satellite microwave remote sensing, because their emissivity is much lower than that of the land surface. To overcome this challenge, we need appropriate representation of heterogeneous land emissivity. We developed a coupled atmosphere and land data assimilation system with the Weather Research and Forecasting Model (CALDAS-WRF), which can assimilate soil moisture, vertically integrated cloud water content over land, and heat and moisture within clouds simultaneously. We applied this system to heavy rain events in Japan. Results show that the system effectively assimilated cloud signals and produced very accurate cloud and precipitation distributions. The system also accurately formed a consistent atmospheric field around the cloud. Precipitation intensity was also substantially improved by appropriately representing the local atmospheric field. Furthermore, combination of the method and operationally analyzed dynamical and moisture fields improved prediction of precipitation duration. The results demonstrate the method's promise in dramatically improving predictions of heavy rain and consequent flooding.

The atmospheric heating process over the Tibetan Plateau (TP) in the premonsoon and mature monsoon seasons of 2008 and 2009 was investigated using radiosonde data and a land data assimilation system coupled with a mesoscale model (LDAS‐A), which assimilates microwave brightness temperature and accurately reproduces land and atmospheric states. Focusing on the temperature observed below 200 hPa, we found that there were warming and cooling periods alternately in the premonsoon season within a general warming trend, and the profiles of heating in the two seasons were reversed. Then we identified the vertical structure of each heating component: sensible heat (SH), latent heat (LH), and horizontal advection (Hadv), using the LDAS‐A in each season. The troposphere over the TP in warming periods was divided into three vertical layers in terms of the major heating process: SH transport below 450 hPa, LH from 450 to 250 hPa, and Hadv above 250 hPa. The SH and LH are transported by local convections. In contrast, the heat source for Hadv originated in the southwest of the plateau, related to synoptic‐scale circulations. Latent cooling with cloud evaporation and adiabatic cooling with convection negatively contributed to heating in the upper troposphere. In cooling periods, the vertical structure of each heating component was similar to that in warming periods, but net heating was reversed because of the influence of synoptic‐scale disturbances. In the mature monsoon season, warm Hadv in the upper troposphere rapidly weakened in response to the initial formation of the Tibetan High.

We proposed a method to estimate cloud water content (CWC) over land using satellite-based passive microwave brightness temperatures (TBs) at multiple-kilometer resolutions with land and atmosphere assimilation to overcome the challenges associated with estimating the CWC of broad cloud systems over land. This method enables estimation of broad cloud systems over land using multifrequency TBs, which have different sensitivities to land and cloud, by concurrently optimizing land emissions and CWC estimates from models. Estimated CWC was validated using vertical two-dimensional CloudSat products (2B-CWC-RVOD and 2C-ICE). The results were in good accordance with 2B-CWC-RVOD in terms of cloud water path and the vertical distribution of CWC but represented underestimates in comparison to 2C-ICE. We performed sensitivity analysis of CWC estimates of TBs and cloud top height. The results suggested that the error in TBs is not large uncertainty, and that cloud top height affects the estimated CWC more sensitively than TBs. The addition of cloud top height information, as determined from CloudSat products as a constraint of optimization, allows further improvement of the vertical distributions of CWC in case studies. Sensitivity analysis indicated that it is effective to utilize cloud top height data from other satellites, such as nextgeneration geostationary meteorological satellites, within an error of about +/- 600 m for further development of our method. This study revealed that the proposed method has great potential to provide unprecedented data for cloud water path and CWC that are continuously distributed over land and ocean with adequate accuracy.

本研究では気象官署，アメダス，解析雨量さらに区内観測などの観測データを用いて過去と現在の一定期間における極端降水頻度を算出し，その変化率を求めた．さらにヨーロッパ全域を対象とした研究で指摘された，極端降水頻度の変化率がClausius-Clapeyron式，約7%°C-1に類似するという仮説が日本でも成り立つのかを調べるため，観測データに戻づく極端降水の変化率とClausius-Clapeyron式で表される変化率を比較し，データや年代，地域を変えて様々な解析を行った．その結果，観測の極端降水頻度の変化率がに類似する結果が多く確認された一方で，2倍の14%°C-1を超える結果も一部確認された．

Satellite-based passive microwave (PMW) remote sensing is an essential technique to clarify long-term and large-scale distribution patterns of cloud water content (CWC). However, most CWC estimation methods are not implemented over land because of high heterogeneity of land radiation, and the detailed characteristics of microwave (MW) radiative transfer between land and atmosphere including clouds have not been elucidated. This study aims to elucidate these characteristics and reveal the accuracy of land emissivity representation necessary for adequate CWC estimation over land using satellite-based PMW under various CWC conditions. First, important parameters related to MW radiative transfer between land and atmosphere at CWC-relevant frequencies in the presence of clouds are determined theoretically. Next, the relationship between errors in these parameters and the brightness temperatures used for CWC estimation is clarified through considerations based on radiative transfer equations. Then, ground-based PMW observations and numerical simulation are used to reveal the actual values of these important parameters and the size of errors. Finally, the results show that for any cloud liquid water path (LWP) value greater than 1.6 kg/m² at 89 GHz and 5 kg/m² at 36 GHz, we can reasonably neglect the heterogeneity of emissivity and radiation from the land surface for CWC estimation. However, when LWP values are below the threshold, the error in the representation of land emissivity should be kept below 0.015 for both 89- and 36-GHz data, and volumetric soil moisture content should have an error lower than 5%-6% for both frequencies.