Precipitation characteristics over Paradeep

The daily rainfall records since 1976 and the SRRG records after its installation in 1982 at Paradeep have been analysed and rainfall climatology has been worked out. The heaviest 24-hour rainfall recorded at the station is 264 mm on 4 June 1982. The mean annual rainfall is 1475 mm. January and December are near dry months while August is the wettest month getting about 339 mm rainfall. The variability of annual rainfall here is 20 %. Light rainspells giving a total rain of 10 mm or less form about 50% occasions in pre-monsoon period and 63% of occasions in monsoon period. The extended rainspells lasting for more than 4 hours form about 10% of occasions in pre-monsoon season and 6% occasions in monsoon season. While morning (04-08 hr IST) period gets the rainfall in both pre-monsoon and monsoon months, early night gets the peak rainfall activity during the pre-monsoon months.

Evaluation of a Physics-Based Tropical Cyclone Rainfall Model for Risk Assessment

Heavy rainfall generated by landfalling tropical cyclones (TCs) can cause extreme flooding. A physics-based TC rainfall model (TCRM) has been developed and coupled with a TC climatology model to study TC rainfall climatology. In this study, we evaluate TCRM with rainfall observations made by satellite (of North Atlantic TCs from 1999 to 2018) and radar (of 36 U.S. landfalling TCs); we also examine the influence on the rainfall estimation of the key input to TCRM—the wind profile. We found that TCRM can simulate relatively well the rainfall from TCs that have a coherent and compact structure and limited interaction with other meteorological systems. The model can simulate the total rainfall from TCs well, although it often overestimates rainfall in the inner core of TCs, slightly underestimates rainfall in the outer regions, and renders a less asymmetric rainfall structure than the observations. It can capture rainfall distribution in coastal areas relatively well but may underestimate rainfall maximums in mountainous regions and has less capability to accurately simulate TC rainfall in higher latitudes. Also, it can capture the interannual variability of TC rainfall and averaged features of the time series of TC rainfall but cannot accurately reproduce the probability distribution of short-term (1 h) rainfall. Among the tested theoretical wind profile inputs to TCRM, a complete wind profile that accurately describes the wind structure in both the inner ascending and outer descending regions of the storm is found to perform the best in accurately generating various rainfall metrics.

Comparison of Rainfall Products over Sub-Saharan Africa

An ever-increasing number of rainfall estimates is available. They are used in many important applications such as flood/drought monitoring, water management, or climate monitoring. Such data are especially valuable in sub-Saharan Africa, where rainfall has considerable socioeconomic impacts and the gauge and radar networks are sparse. The choice of a rainfall product can significantly influence the performance of such applications. This study reviews previous works, evaluating or comparing rainfall products over different parts of sub-Saharan Africa. Three types of rainfall products are considered: the gauge-only, the satellite-based, and the reanalysis ones. In addition to the global rainfall products, we included three regional ones specifically developed for Africa: the African Rainfall Climatology version 2 (ARC2), the Rainfall Estimate version 2 (RFE2), and the Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) African Rainfall Climatology and Time Series (TARCAT). The gauge density, the orography, and the rainfall regime, which vary with the climate and the season, influence the performance of the rainfall products. This review does not focus on comparing results, as many other publications doing so are already available. Instead, we propose this review as a guide through the different rainfall products available over Africa, and the factors influencing their performances. With this review, the reader can make informed decisions about which products serve their specific purpose best.

Revisiting the statistical scaling of peak annual discharges with respect to the basin size in the light of rainfall climatology

<p>Over the years, several studies have been carried out to investigate how the statistics of peak annual discharges vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e. simple scaling vs multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, two important questions arise: a) how rainfall climatology affects the scaling of peak annual discharges, and b) how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing these two questions, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. In doing so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: a) the local rainfall climatology is an important contributor to the observed statistics of annual peak discharges, and b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that of the underlying rainfall process, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light to the approximate type of scaling of peak annual discharges with the basin size.</p>

Revisiting the Statistical Scaling of Annual Discharge Maxima at Daily Resolution with Respect to the Basin Size in the Light of Rainfall Climatology

Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, an important question arises on how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing this question, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. To do so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: (a) the local rainfall climatology is an important contributor to the observed statistics of peak annual discharges, and (b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that commonly met in actual rainfields, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light on the approximate type of scaling of annual discharge maxima with the basin size.