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.
[1] 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.Citation: Seto, R., T. Koike, and M. Rasmy (2013), Analysis of the vertical structure of the atmospheric heating process and its seasonal variation over the Tibetan Plateau using a land data assimilation system,
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 next‐generation 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.
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