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Remote Sens. 2014, 6(6), 5306-5324; doi:10.3390/rs6065306

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

1
Department of Civil Engineering, The University of Tokyo, Tokyo 113-8656, Japan
2
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Received: 7 February 2014 / Revised: 23 May 2014 / Accepted: 26 May 2014 / Published: 6 June 2014
(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)
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Abstract

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. View Full-Text
Keywords: passive microwave remote sensing; data assimilation; numerical forecast; soil moisture; clouds; precipitation passive microwave remote sensing; data assimilation; numerical forecast; soil moisture; clouds; precipitation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Rasmy, M.; Koike, T.; Li, X. Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa. Remote Sens. 2014, 6, 5306-5324.

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