The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin

Abstract

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort.

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

“…Satellite-based products can provide worldwide estimates of precipitation at acceptable spatial and temporal scales. Previously, several satellite-based precipitation products (SPPs) were introduced [7][8][9][10]. The latest SPP precipitation estimating algorithms are capable of providing continuous precipitation information at fine spatial and temporal resolutions, using data from either infrared (IR) or microwave (MW) sensors.…”

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

“…Although the performance of the selected soil moisture-based precipitation is better on the monthly scale and can be employed for hydro-climatic applications, the daily estimations were very poor against the gauge-daily estimations. Therefore, the algorithm retrievals of SPPs should be enhanced by applying advanced techniques and models [9,43,44] (data-driven approaches such as machine learning/deep learning, downscaling of precipitation products, bias correction of SPPs) for more efficient utilization of satellite data. Moreover, sub-daily data were not available in the research, and the lowest temporal scale used in this study to examine the performances of four SPPs was daily resolution.…”

“…Precipitation data is important for reliable prediction to form hydro climatological studies. Therefore, validating SPPs accuracy is necessary before using them effectively in a variety of hydro-climatological analyses [9]. On the other hand, SPPs can deliver constant information on precise spatial and temporal scales [10].…”

“…All the SPPs data has been accessed on 25 September 2022. In recent times, many satellite-based precipitation products were introduced to obtain reliable precipitation [9,[21][22][23]. Most of the precipitation satellites were assessed in the upper Indus region and some were evaluated in the mountainous region of Pakistan [5,22,24].…”