Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Abstract

The use of meteorological satellites for rainfall estimation and monitoring was introduced as a way of augmenting conventional ground-based rainfall data for hydrological models and weather forecasting. Today the primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence, amount and distribution over the globe for a number of applications such as meteorology at all scales, climatology, hydrology and environmental sciences. The uneven distribution of raingauges and weather radars and the lack of rainfall data over the oceans have always been a concern and until now the rainfall, as a prominent branch of the global hydrological cycle, is not well understood. In this sense the problem is not different from the determination of wind, pressure, temperature and humidity fields although precipitation is by far the most variable in space and time. Furthermore, unlike many other atmospheric phenomena, precipitation (or the lack of it) has a direct impact on human life (e.g. flash floods, Barrett & Michell, 1991). Therefore, satellite monitoring is used to address the key questions of spatial and temporal coverage, which cannot be achieved by other observing systems.Among the challenges that face the science and technology of satellite remote sensing the quantitative determination of rainfall from the variety of precipitating systems is one of the most difficult and is largely unsolved. Barrett & Martin (1981) and Kidder & Vonder Haar (1995) give excellent reviews of the methods available. Petty (1995) has examined the status of satellite rainfall estimation over land. A recent review by Levizzani (1998a) has covered results and future perspectives from the geostationary orbit. The perspecMeteorol. Appl. 8, 23-41 (2001) Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

“…The VIS precipitation retrieval method mainly utilizes the empirical relationship between cloud brightness and rainfall because precipitation is considered to occur under highly bright clouds, which can be associated with cloud types, cloud areas and cloud growth rates [4][5][6]. IR imagery is associated with cloud top temperatures and cloud growth rates can be obtained via the thermal emissions during both night and day; generally, heavier rainfall tends to be associated with larger, taller clouds with colder cloud tops [5].…”

“…IR imagery is associated with cloud top temperatures and cloud growth rates can be obtained via the thermal emissions during both night and day; generally, heavier rainfall tends to be associated with larger, taller clouds with colder cloud tops [5]. Many researchers have developed different methods to retrieve precipitation based on VIR/IR data from geosynchronous earth orbit (GEO) and low-earth orbit (LEO) satellites, including the 3-hourly and monthly mean rainfalls [6][7][8][9][10]. MW sensors can detect rain clouds directly and can provide information about the atmospheric constituents and hydro-meteorological profiles, which are more directly related to the ground precipitation rate [11,12].…”

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

“…Such IR retrievals are appropriate for deep convective clouds that can be easily identified in the infrared and/or water vapour channels (e.g. Levizzani et al, 2001;Levizzani, 2003) but show considerable drawbacks in the mid-latitudes (e.g. Ebert et al, 2007;Früh et al, 2007), where great parts of the precipitation originates from clouds formed by widespread frontal lifting processes in connection with extra-tropical cyclones (hereafter denoted as advectivestratiform precipitation).…”

Neural networks in satellite rainfall estimation

“…If any real time operation is intended, for example the use of Meteosat Second Generation data (15 minutes of temporal resolution) to derive rainfall rates, the improvement in the processing speed can be crucial (Levizzani et al 2001).…”