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Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests

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Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests NOVEMBER 2014 K ÜHNLEIN ET AL. 2457 Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests MEIKE KÜHNLEIN AND TIM APPELHANS Environmental Informatics, Faculty of Geography, Philipps-University Marburg, Marburg, Germany BORIS THIES Laboratory for Climatology and Remote Sensing, Faculty of Geography, Philipps-University Marburg, Marburg, Germany THOMAS NAUß Environmental Informatics, Faculty of Geography, Philipps-University Marburg, Marburg, Germany (Manuscript received 27 February 2014, in final form 4 August 2014) 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 perfor- mances 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. 1. Introduction unavailable in many regions. Within this context, pre- cipitation retrievals from optical sensors aboard geo- Various investigations within biodiversity and ecological- stationary (GEO) weather satellites may fill the gap by oriented projects require area-wide precipitation informa- providing area-wide information about rainfall distri- tion at high temporal and spatial resolution. However, bution and the amount at high spatial and temporal despite its great importance, the high variability in space, resolution. time, and intensity of this parameter still impedes its cor- Traditionally, the spectral resolution of optical sen- rect spatiotemporal detection and quantification. More- sors available on GEO platforms was rather poor. This over, rain gauges or ground-based radar networks, which restriction only allowed schemes that rely on a relation- are generally used to observe precipitation, are sparse or ship between cloud-top temperatures measured in an infrared (IR) channel and rainfall probability/intensity Corresponding author address: Meike Kühnlein, Philipps- (e.g., Adler and Mack 1984; Arkin and Meisner 1987). University Marburg, 35037 Marburg, Germany. Such IR retrievals are most applicable for convective E-mail: [email protected] clouds that can be easily identified in the IR and/or DOI: 10.1175/JAMC-D-14-0082.1 Ó 2014 American Meteorological Society Unauthenticated | Downloaded 10/02/21 10:09 PM UTC 2458 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 53 water vapor channels and, thus, work best in the tropics models. Moreover, these methods are only convenient (e.g., Levizzani et al. 2001; Levizzani 2003). However, for use with a few input variables. To address the con- they show considerable drawbacks concerning the sideration of multiple input variables, as well as to avoid detection and quantification of rainfall from advective- the assumption of parametric tests and underlying con- stratiform clouds in connection with extratropical cy- ceptual models, it is necessary to consider other tech- clones (e.g., Ebert et al. 2007; Früh et al. 2007). This type niques. In this context, machine-learning algorithms of cloud is characterized by relatively warm and spatially have been successfully adapted to remote sensing and homogeneous cloud-top temperatures that do not differ rainfall applications (Islam et al. 2014b; Giannakos and significantly from raining to nonraining regions. Strati- Feidas 2013; Islam et al. 2012a,b; Rivolta et al. 2006; form rain is usually not associated with high rainfall Capacci and Conway 2005; Grimes et al. 2003; Hsu et al. rates, but it often covers large areas and therefore con- 1997), which show that machine-learning algorithms tributes to a significant portion of the rainfall in a region. might be suitable for helping to overcome these limita- As a result, the use of retrieval techniques based solely tions. In particular, recent developments in parallel on IR cloud-top temperature leads to an underestima- computing with machine learning offer new possibilities tion of the detected precipitation area and to uncertainties in terms of training and predicting speed and therefore concerning the assigned rainfall rate in such cases. make the usage and improvement of real-time systems The enhanced spectral resolution of more recent feasible. GEO systems along with enhanced information content In recent years, the random forest (RF) machine- on cloud properties led several authors to suggest the learning technique (Breiman 2001) has received utilization of optical and microphysical cloud parame- increasing interest. This ensemble classification and ters derived from now-available multispectral datasets regression technique is based on the assumption that to improve optical rainfall retrievals (e.g., Rosenfeld a whole set of trees or networks produce a more accurate and Gutman 1994; Ba and Gruber 2001; Rosenfeld and prediction than does a single tree or network (Dietterich Lensky 1998; Nauss and Kokhanovsky 2006; Roebeling 2002). It is one of the most accurate learning algorithms and Holleman 2009; Kühnlein et al. 2010; Feidas and available and although RF has been shown to perform Giannakos 2011; Thies et al. 2008a–c). They were able to very well in a variety of environmental investigations show that cloud areas with a high optical thickness and (Islam et al. 2014a; Rodriguez-Galiano et al. 2012; Guo a large effective particle radius possess a high amount of et al. 2011; Ghimire et al. 2010; Cutler et al. 2007; Prasad cloud water and are characterized by a higher rainfall et al. 2006; Pal 2005; Mota et al. 2002), the utilization of probability and intensity than cloud areas with a low RF in atmospheric sciences remains rare. Yet, it offers optical thickness and a small effective particle radius. In a number of features that make it well suited for use in addition to the use of optical and microphysical cloud remote sensing applications (e.g., it runs efficiently on parameters, many of the retrieval techniques make use large datasets, it can capture nonlinear association pat- of convective and stratiform precipitation area classifi- terns between predictors and response). cation schemes to improve the accuracy of the satellite In summary, the encouraging results concerning rain rainfall estimation (e.g., Adler and Negri 1988; Arkin area detection, rain process separation, and rainfall-rate and Meisner 1987; Anagnostou and Kummerow 1997). assignment, along with the enhanced information con- Within this context, Thies et al. (2008c) used cloud tent on cloud properties at high spectral, and spatial and properties retrieved from Meteosat Second Generation temporal resolution, point to the quite promising po- (MSG) Spinning Enhanced Visible and Infrared Imager tential of current and upcoming GEO systems as the (SEVIRI) data to separate areas of differing precip- basis for reliable rainfall retrievals. However, there is itation processes within the rain area as part of a satellite- still a great deficit regarding the detection of rain areas based retrieval scheme during day and night. Recently, and the assignment of rainfall rates in the midlatitudes, Feidas and Giannakos (2012) and Giannakos and Feidas especially in connection with extratropical cyclones in (2013) introduced techniques that classify convective and a continuous manner (day, night, twilight), resulting in stratiform precipitation areas based on spectral and tex- a 24-h estimation at high temporal resolution. In par- tural features of MSG SEVIRI data. ticular, all existing optical retrievals that are based on So far, most of the retrieval techniques use parametric optical and microphysical cloud parameters are restricted approaches to relate cloud properties to precipitation to daytime and nighttime conditions and do not cover (e.g., Adler and Negri 1988; Cheng and Brown 1995; twilight conditions (see, e.g., Kidd and Levizzani 2011). Kühnlein et al. 2010; Levizzani et al. 1990; Thies et al. Thus, the potential offered by machine-learning ap- 2008c). These approaches require the specification of proaches will likely benefit from the potential offered by the underlying parametric
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