Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau

Rasmy, Mohamed; Koike, Takao; Kuria, David Ndegwa; Mirza, Cyrus Raza; Li, Xiaoqiong; Yang, Kun

doi:10.1109/tgrs.2012.2190517

Cited by 20 publications

(17 citation statements)

References 48 publications

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“…Similarly, the only difference between LDAS-A and CALDAS simulations is that CALDAS used additional satellite observed cloud data. Detailed information and formulations on LDAS-A and CALDAS are given in [11,12], respectively; however, the following sections provide a brief explanation of their formulation.…”

Section: Modelsmentioning

confidence: 99%

“…They began to form at the cloud tops, grew to their maximum at the cloud bottoms, and subsequently decreased because of evaporation and the breakup of the raindrops or snowflakes. Further information on the model and the setup can be referenced from [12].…”

Section: Study Domain and Model Configurationmentioning

confidence: 99%

“…As a continuation from our previous study [12], this work addresses the application of CALDAS as a weather forecasting system, simulating short-term weather conditions over a mesoscale domain of Niger in West Africa. Model performance is investigated using available ground-based data and satellite observations.…”

Section: Introductionmentioning

confidence: 98%

“…To overcome such problems, an on-line system (i.e., Land Data Assimilation System coupled with Atmospheric model (LDAS-A)) was developed to directly assimilate lower frequency microwave radiance from the Advanced Microwave Scanning Radiometer (AMSR) for updating realistic soil moisture content in a mesoscale model and the results were presented in [11]. However, because of limited satellite observations (e.g., AMSR-E and AMSR-2, twice daily maximum), the improved land surface conditions often suffered from significant errors and drift due to biases in the predicted forcing (e.g., rainfall and solar radiation) that misguided the subsequent forecast [11,12]. Thus, this particular problem rendered the on-line land data assimilation ineffective in NWP models.…”

Section: Introductionmentioning

confidence: 99%

“…CALDAS assimilates lower frequency data of AMSR-E/AMSR-2 to improve the representation of land surface conditions and it merges them with higher frequency data of AMSR-E/AMSR-2 to improve the representation of cloud conditions over land surfaces. CALDAS results from the Tibetan Plateau have shown that simultaneous assimilation of land and cloud conditions improved the biases in land surface states, cloud representation, and forcing to land surface models, which enhanced land-atmosphere interactions [12].…”

Section: Introductionmentioning

confidence: 99%

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Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

Rasmy

Koike

2014

Remote Sensing

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The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive microwave data that contain cloud information is very limited over land because of strong heterogeneous land surface emissions. The Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed by merging soil moisture information estimated from low-frequency data with corresponding high-frequency data to estimate cloud information and, thus, improve weather forecasting over Niger, West Africa. The results showed that the assimilated soil moisture and cloud distributions were reasonably comparable to satellite retrievals of soil moisture and cloud observations. However, assimilating soil moisture alone within a mesoscale model produced only marginal improvements in the forecast, whereas the assimilation of both soil moisture and cloud distributions improved the simulation of temperature and humidity profiles. Rainfall forecasts from CALDAS also correlated well with satellite retrievals. This indicates the potential use of CALDAS as a reliable forecasting tool for developing regions. Further developments of CALDAS and the inclusion of data from several other sensors will be researched in future studies.Remote Sens. 2014, 6 5307

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Section: Modelsmentioning

confidence: 99%

Section: Study Domain and Model Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

Rasmy

Koike

2014

Remote Sensing

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Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

Lin

Ebtehaj

Wang

et al. 2017

Water Resources Research

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This study characterizes the space-time structure of soil moisture background error covariance and paves the way for the development of a soil moisture variational data assimilation system for the Noah land surface model coupled to the Weather Research and Forecasting (WRF) model. The soil moisture background error covariance over the contiguous United States exhibits strong seasonal and regional variability with the largest values occurring in the uppermost soil layer during the summer. Large background error biases were identified, particularly over the southeastern United States, caused mainly by the discrepancy between the WRF-Noah simulations and the initial conditions derived from the used operational global analysis data set.

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Heavy rainfall prediction applying satellite‐based cloud data assimilation over land

Seto

Koike

Rasmy³

2016

JGR Atmospheres

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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.

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Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau

Cited by 20 publications

References 48 publications

Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

Heavy rainfall prediction applying satellite‐based cloud data assimilation over land

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