Laboratory for
Climatology and Remote Sensing

Abstract:

The island of Corsica in the western Mediterranean is characterized by a pronounced topography in which local breeze systems develop in the diurnal cycle. In interaction with the large-scale synoptic situation, various precipitation events occur, which are classified in this study with regard to their duration and intensity. For this purpose, the island was grouped into five precipitation regimes using a cluster analysis, namely the western coastal area, the central mountainous region, the southern coastal area, the northeast coastal area, and the eastern coastal area. Based on principal component analysis using mean sea level pressure (mslp) obtained from ERA5 reanalysis (the fifth generation of the European Centre for Medium-Range Weather Forecasts, ECMWF), six spatial patterns were identified which explain 98% of the large-scale synoptic situation, while the diurnal breeze systems within the regimes characterize local drivers. It is shown that on radiation days with weak large-scale pressure gradients, pronounced local circulations in mountainous regions are coupled with sea breezes, leading to a higher number of short and intense precipitation events. Meridional circulation patterns lead to more intensive precipitation events on the eastern part of the island (30% intensive events with meridional patterns on the east side compared to 11% on the west side). On the west side of Corsica, however, coastal precipitation events are seldom and less intense than further inland, which can be attributed to the influence of the topography in frontal passages.

Abstract:

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.

Abstract:

Knowledge of the spatio-temporal precipitation distribution is of great value in agriculture, water engineering, climatology and risk management. So far, no adequate method existed for the detection and monitoring of precipitation at high temporal and spatial resolutions in most parts of the world where radar networks are not available. Due to spectral constraints, existing retrieval techniques rely on a relationship between rainfall probability and intensity and the cloud top temperature measured in an infrared channel. These techniques show considerable drawbacks concerning precipitation processes in the mid-latitudes. Improved techniques for rain area identification based on spectral enhancements of new generation satellite systems used to be only available on polar orbiting platforms with poor temporal resolutions. Furthermore, these algorithms are only applicable during day-time. With the advent of Meteosat Second Generation (MSG) Spinning-Enhanced Visible and InfraRed Imager (SEVIRI) in 2004, a geostationary satellite system with significantly improved spectral and spatial resolutions has become available. The central aim of the present study therefore was to develop a novel method for operational precipitation detection during day- and night-time based on MSG SEVIRI data. The focus of the newly developed scheme lies on precipitation processes in the mid-latitudes in connection with extra-tropical cyclones. It is therefore not only applicable to convectively dominated rain areas but also to precipitating cloud areas of advective-stratiform character. The newly developed rainfall retrieval scheme based on the advanced second-generation GEO system MSG SEVIRI rests upon the following conceptual model: • Precipitating cloud areas are characterized by a sufficiently high cloud water path and ice particles in the upper part. • Cloud areas with higher rainfall intensities are characterized by a higher cloud water path and a higher amount of ice particles in the upper part. • Convective clouds with very high rainfall intensities are characterized by a large vertical extension and a high rising cold cloud top. Based on this conceptual design, the new retrieval scheme consists of an entirely new methodology compiling novel and innovative algorithms and approaches. The following three components are the focal parts of the novel technique: • A new algorithm for the identification of the rain area during day- and night-time was developed for SEVIRI. The method allows not only a proper detection of mainly convective rain areas but also enables the detection of advective-stratiform precipitation (e.g. in connection with mid-latitude frontal systems). It is based on information about the CWP and the cloud phase in the upper cloud regions. • An infrared retrieval technique appropriate for convective precipitation processes in the mid-latitudes was successfully transferred and adapted to MSG SEVIRI. The phenomenon of positive brightness temperature differences between the WV and IR channels (dTWV-IR), which enables the detection and classification of convectively dominated raining cloud areas was investigated for the WV and IR channels of SEVIRI. Based on radiative transfer calculations, which revealed the existence of positive ΔTWV-IR for all SEVIRI WV-IR differences, the dTWV technique could be applied and transferred to SEVIRI. • A new technique for precipitation process and rainfall intensity separation was developed for SEVIRI. The process separation and the further subdivision relies on information about the cloud top height, the cloud water path and the cloud phase in the upper parts. The subdivision is realized in a stepwise manner. In a first step the rain area is separated into the subareas of convective and advective-stratiform precipitation processes. In the following both separated process areas are divided into subareas of differing rainfall intensities. The process separation and the subdivision of the convective precipitation area relies on information about the cloud top height. The subdivision of the advective-stratiform precipitation area is based on information about the CWP and the particle phase in the upper parts of the cloud. The rain area and the process-oriented rainfall intensities detected and classified by the newly developed retrieval technique were validated against corresponding ground-based radar data of Germany, representative for mid-latitude precipitation processes. The results of the validation study indicate persuading performance of the new algorithm concerning rain area identification as well as process and intensity differentiation and indicate the stability of the introduced conceptual design.

Abstract:

Die Arbeit stellt die Entwicklung eines Verfahrens zur Erkennung der Niederschlagsfläche in optischen Satellitendaten dar. Die Abgrenzung regnender Wolken beruht dabei auf dem Modellkonzept, dass diese über eine ausreichend große Kombination aus optischer Dicke und effektivem Tropfenradius verfügen müssen.