Probabilistic forecast of daily areal precipitation focusing on extreme events

A dynamical downscaling scheme is usually used to provide a short range flood forecasting system with high-resolved precipitation fields. Unfortunately, a single forecast of this scheme has a high uncertainty concerning intensity and location especially during extreme events. Alternatively, statistical downscaling techniques like the analogue method can be used which can supply a probabilistic forecasts. However, the performance of the analogue method is affected by the similarity criterion, which is used to identify similar weather situations. To investigate this issue in this work, three different similarity measures are tested: the euclidean distance (1), the Pearson correlation (2) and a combination of both measures (3). The predictor variables are geopotential height at 1000 and 700 hPa-level and specific humidity fluxes at 700 hPa-level derived from the NCEP/NCAR-reanalysis project. The study is performed for three mesoscale catchments located in the Rhine basin in Germany. It is validated by a jackknife method for a period of 44 years (1958–2001). The ranked probability skill score, the Brier Skill score, the Heidke skill score and the confidence interval of the Cramer association coefficient are calculated to evaluate the system for extreme events. The results show that the combined similarity measure yields the best results in predicting extreme events. However, the confidence interval of the Cramer coefficient indicates that this improvement is only significant compared to the Pearson correlation but not for the euclidean distance. Furthermore, the performance of the presented forecasting system is very low during the summer and new predictors have to be tested to overcome this problem

Atmospheric circulation patterns that trigger heavy rainfall in West Africa

Classification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two-step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan-Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large-scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule-based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso-scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region

Classification of atmospheric circulation patterns that trigger rainfall extremes in the Sudan-Sahel region

A better understanding of the rainfall variability and extremes in tropical regions is crucial for the development of improved statistical and numerical approaches used for climate research and weather prediction. In this study, we present a novel fuzzy rule-based method for classifying atmospheric circulation patterns relevant to heavy rainfall in the Sudan-Sahel region over West Africa. In the first step, we determine large-scale atmospheric patterns to describe important seasonal features of the West African Monsoon like the movement of Saharan Heat Low over the African continent. In the second step, meso-scale monsoon patterns are classified to better describe rainfall variability and extremes during the monsoon period. In addition to a comprehensive predictor screening using more than 30 variables at different atmospheric levels, a detailed sensitivity analysis is performed, which aims to improve the transferability of the classification approach to an independent dataset. Furthermore, crucial aspects of the methodological development of fully automatic classification approaches are addressed. Using mean sea level pressure and stream function fields (700hPa) as final predictor variables, we identified 23 circulation patterns as robust solution to represent key atmospheric processes and rainfall variability in the study region. The two wettest patterns are distinguished by an enhanced Saharan Heat Low and cyclonic rotation near the study region, suggesting the presence of a tropical wave trough and triggering about 50% of the rainfall extremes on 6.5% of the days. The identified atmospheric circulation patterns are currently used to develop a variety of improved statistical approaches for this challenging region, such as pattern-dependent bias correction, geostatistical interpolation, and simulation

Atmospheric circulation patterns that trigger heavy rainfall in West Africa

Classification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two-step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan-Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large-scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule-based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso-scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region

Stochastic simulation of daily precipitation extremes in West Africa [Abstract]

West Africa is one of the most data-poor regions in the world. In-situ precipitation observations are not available for many sites or contain many data gaps, thus leading to uncertainties and biases in hydrological studies in this region. To address this fundamental problem, we present a straightforward stochastic approach based on turning bands to simulate daily precipitation fields. Our approach is based on meta-Gaussian frameworks that generate Gaussian random fields, which are transformed into "real-world" precipitation fields using transfer functions. The simulation approach is tested for multiple extremes (1991 – 2016) in the Ouémé river basin in West Africa using different model settings and the most comprehensive station-based precipitation dataset available for this region. The evaluation shows that our approach is a valuable tool for simulation of daily precipitation fields and clearly outperforms classical interpolation techniques (e.g., ordinary kriging). Moreover, the simulation method can be conditioned on observations, uses only a small set of parameters and is an efficient algorithm for ensemble generation of precipitation fields for ungauged areas and design events. In our West African research projects FURIFLOOD, the precipitation simulations are used as input information for hydrological modeling to reconstruct observed flood events and to create improved hazard maps for this region. Overall, the application of this advanced technique contributes to a better understanding of precipitation uncertainties and to the provision of improved station-based precipitation products for this challenging region

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

West Africa is a hot spot region for land–atmosphere coupling where atmospheric conditions and convective rainfall can strongly depend on surface characteristics. To investigate the effect of natural interannual vegetation changes on the West African monsoon precipitation, we implement satellite-derived dynamical datasets for vegetation fraction (VF), albedo and leaf area index into the Weather Research and Forecasting model. Two sets of 4-member ensembles with dynamic and static land surface description are used to extract vegetation-related changes in the interannual difference between August–September 2009 and 2010. The observed vegetation patterns retain a significant long-term memory of preceding rainfall patterns of at least 2 months. The interannual vegetation changes exhibit the strongest effect on latent heat fluxes and associated surface temperatures. We find a decrease (increase) of rainy hours over regions with higher (lower) VF during the day and the opposite during the night. The probability that maximum precipitation is shifted to nighttime (daytime) over higher (lower) VF is 12 % higher than by chance. We attribute this behaviour to horizontal circulations driven by differential heating. Over more vegetated regions, the divergence of moist air together with lower sensible heat fluxes hinders the initiation of deep convection during the day. During the night, mature convective systems cause an increase in the number of rainy hours over these regions. We identify this feedback in both water- and energy-limited regions of West Africa. The inclusion of observed dynamical surface information improved the spatial distribution of modelled rainfall in the Sahel with respect to observations, illustrating the potential of satellite data as a boundary constraint for atmospheric models

Towards a historical precipitation database for West Africa: overview, quality control and harmonization

Reliable long-term observations from precipitation stations are often required for climatological studies but are strongly limited in many regions of the world. To improve this limitation for West Africa, we compiled daily and monthly observations from more than 20 national, continental and global databases, to establish a historical precipitation archive with a focus on four countries (Burkina Faso, Ghana, Benin and Togo). The new archive contains long-term daily and monthly precipitation measurements from 1819 to 2013 for more than 1,000 sites. It is, therefore, the most comprehensive historical dataset with daily and monthly precipitation observations for this region. To produce a quality-controlled and harmonized precipitation dataset for the focal region, various statistical algorithms have been implemented. These algorithms rely on straightforward geostatistical approaches (e.g., spatial correlograms) and corresponding statistical tests for identification and elimination of unreliable time series, in addition to various standard approaches used by global data centers. Although the quality control revealed various data errors and uncertainties for measurements and meta-information (e.g., unit conversion errors, temporal offsets, frequent and long data gaps), a spatial interpolation using the quality-controlled and harmonized dataset produced relatively reliable precipitation patterns for different target variables (e.g., monthly precipitation amount and daily precipitation probability). A major remaining challenge is providing free access to this database for research and other noncommercial purposes, due to national data protection regulations. However, several further tasks have been initiated and implemented (e.g., free provision of gridded precipitation datasets and point statistics) to improve the access and availability of station-based precipitation observations and related data products for this climatologically challenging region