Laboratory for
Climatology and Remote Sensing

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:

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It wa developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieva technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Abstract:

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Dierent time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with dierent features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant dierence was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the AndesMountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Abstract:

Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation.Meanwhile, in UMregions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones fromUMto BMand zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.

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.