Abstract
This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.
Highlights
Accurate rainfall detection and estimation in space and time are essential for resolving scientific questions, for operational purposes such as early warning, forecasting and the development of services and applications that affect and influence human welfare and agricultural production decisions at a farm-scale level [1,2,3,4]
A general observation from Figure 3 is the clear distinction in the peak of the distribution for the raining and non-raining densities. This characteristic behaviour is supported by the significant difference in descriptive statistics (Appendix A, Tables A1 and A2, respectively) computed from the raining and non-raining data. This observation is noticeable in the reflectance than in the brightness temperature (BT) and brightness temperature differences (BTD) plots, it raises the possibility of rain and no-rain discrimination by using respective thresholds in the 2-D space
The detection of rain areas using multispectral satellite data from Meteosat Second Generation (MSG) SEVIRI was demonstrated for the daytime and nighttime over topographically complex terrain in southwestern Kenya
Summary
Accurate rainfall detection and estimation in space and time are essential for resolving scientific questions, for operational purposes such as early warning, forecasting and the development of services and applications that affect and influence human welfare and agricultural production decisions at a farm-scale level [1,2,3,4]. Due to the high spatial and temporal variability of rainfall, accurate detection and estimation are still open research challenges. The use of geostationary weather satellites for rainfall detection and estimation has gained research attention because they can be used to retrieve area-wide distribution of this spatiotemporally varying phenomena at a high temporal resolution. The single IR retrieval technique shows drawbacks when applied to detect and quantify rainfall from stratiform clouds. These clouds have spatially homogenous (warmer) temperatures that do not differ significantly between raining and non-raining clouds. A single IR based technique leads to uncertainties in the detected rain areas and rain rates [10]. IR-only satellite rainfall products such as [7] were developed for operational application in Africa; ground validation over the years have shown varying accuracies over the continent see, e.g., [11,12]
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