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A new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3–8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data.

Due to the significant impact of fog and low stratus (FLS) on economy, ecology and traffic systems, there is a growing demand for high-resolution information on FLS occurrence. In this study, a baseline climatology of FLS h day −1 based on data recorded from 2006-2015 by the Spinning Enhanced Visible and Infrared Imager system (SEVIRI) aboard the Meteosat Second Generation satellites is computed for Europe to provide the requested information. It is the first 10 year, spatially explicit climatology for FLS based on data with a temporal resolution of 15 min. The dataset is validated against Meteorological Aviation Routine Weather Reports (METAR) and shows good accordance with an average Heidke Skill Score of 0.45. Temporal and spatial variations in FLS frequency as well as interannual trends are analyzed. Winter shows the highest FLS occurrence, but a general decrease over the investigated period. Spring, summer and autumn show less pronounced trends and lower average FLS frequencies. Possible reasons for these distributions are discussed.

[1] A new day and night technique for precipitation process separation and rainfall intensity differentiation using the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager is proposed. It relies on the conceptual design that convective clouds with higher rainfall intensities are characterized by a larger vertical extension and a higher cloud top. For advective-stratiform precipitation areas, it is assumed that areas with a higher cloud water path (CWP) and more ice particles in the upper parts are characterized by higher rainfall intensities. First, the rain area is separated into areas of convective and advective-stratiform precipitation processes. Next, both areas are divided into subareas of differing rainfall intensities. The classification of the convective area relies on information about the cloud top height gained from water vapor-IR differences and the IR cloud top temperature. The subdivision of the advective-stratiform area is based on information about the CWP and the particle phase in the upper parts. Suitable combinations of temperature differences (DT 3.9 -10.8 , DT 3.9 -7.3 , DT 8.7 -10.8 , DT 10.8 -12.1 ) are incorporated to infer information about the CWP during nighttime, while a visible and a near-IR channel are considered during the daytime. DT 8.7 -10.8 and DT 10.8 -12.1 are particularly included to supply information about the cloud phase. Intensity differentiation is realized by using pixel-based confidences for each subarea calculated as a function of the respective value combinations of the previously mentioned variables. For the calculation of the confidences, the value combinations are compared with ground-based radar data. The proposed technique is validated against ground-based radar data and shows an encouraging performance (Heidke skill score 0.07-0.2 for 15-min intervals).

ABSTRACT:A new method for the delineation of precipitation during night-time using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud-top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid-latitude frontal systems).The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension [both represented by the cloud water path (CWP)], and the existence of ice particles in the upper part of the cloud. As no operational retrieval exists for Meteosat Second Generation (MSG) to compute the CWP during night-time, suitable combinations of brightness temperature differences ( T ) between the thermal bands of Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI, T 3.9 -10.8 , T 3.9 -7.3 , T 8.7 -10.8 , T 10.8 -12.1 ) are used to infer implicit information about the CWP and to compute a rainfall confidence level. T 8.7 -10.8 and T 10.8 -12.1 are particularly considered to supply information about the cloud phase.Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the channel differences, the value combination of the channel differences is compared with ground-based radar data. The retrieval is validated against independent radar data not used for deriving the transfer function and shows an encouraging performance as well as clear improvements compared to existing optical retrieval techniques using only IR thresholds for cloud-top temperature.