scholarly journals Validation of Satellite Rainfall Estimates over Equatorial East Africa

2021 ◽  
Keyword(s):  
East Africa ◽  
Prediction Models ◽  
Extreme Event ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Extreme Rainfall Events ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–2018, this study performs a multi-scale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual timescales, for extreme daily precipitation, and for TMPA and IMERG near real-time (NRT) products. Results show that: 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual timesteps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights; improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.

Validation of Satellite Rainfall Estimates over Equatorial East Africa

2021 ◽  
Author(s):  
Simon Ageet ◽  
Andreas Fink ◽  
Marlon Maranan
Keyword(s):  
East Africa ◽  
Extreme Values ◽  
Extreme Event ◽  
Weather Prediction ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Extreme Rainfall Events ◽  
Satellite Rainfall ◽  
Rainfall Estimates

<p>The sparsity of rain gauge (RG) data over Africa is a known impediment to the assessments of hydro-meteorological risks and of the skill of numerical weather prediction (NWP) models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms.  Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–2018, this study performs a multi-scale evaluation of gauge-calibrated SREs, namely, Integrated Multi-satellite Retrieval for Global Precipitation Measurement (GPM) (IMERG), Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). Skills were assessed from daily to annual timescales, for extreme daily precipitation, and for the TMPA and IMERG near real-time (NRT) products. Results show that: 1) the satellite products reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best overall for shorter temporal scales (daily, pentadal and dekadal) while MSWEP and CHIRPS perform best at the monthly and annual timesteps, respectively; 3) the SREs’ performance, especially in MSWEP, shows high spatial variability likely due to the variation of weights assigned during gauge calibration; 4) all the SREs miss between 57% (IMERG NRT)  and 83 (CHIRPS) of daily extreme rainfall events recorded in the RGs; 5) IMERG NRT outperforms all the other products regarding extreme event detection and accuracy; and 6) for assessing return values of daily extreme values, IMERG and MSWEP are satisfactory while the use of CHIRPS cannot be recommended. The study highlights some improvements of IMERG over its predecessor TMPA and the potential of Multi-Source Weighted-Ensembles products such as MSWEP for flood risk assessment and validation of NWP rainfall forecasts over East Africa.</p>

 Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations 

2021 ◽  
Author(s):  
Ajay Bankar ◽  
Rakesh Vasudevan
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Distinct Advantage ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Synoptic Conditions

<p><span>Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE’s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.</span></p>

Extreme Rainfall Events in the Macro-Metropolis of São Paulo: trends and connection with climate oscillations

2021 ◽  
Author(s):  
Carolyne B. Machado ◽  
Thamiris L. O. B. Campos ◽  
Sameh A. Abou Rafee ◽  
Jorge A. Martins ◽  
Alice M. Grimm ◽  
...  
Keyword(s):  
Urban Areas ◽  
Extreme Rainfall ◽  
Kendall Test ◽  
Rain Gauge ◽  
Sao Paulo ◽  
Annual Rainfall ◽  
São Paulo ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Climatic Indices

AbstractIn the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

scholarly journals Hydro-meteorological reconstruction and geomorphological impact assessment of the October 2018 catastrophic flash flood at Sant Llorenç, Mallorca (Spain)

2019 ◽  
Vol 19 (11) ◽  
pp. 2597-2617 ◽  
Author(s):  
Jorge Lorenzo-Lacruz ◽  
Arnau Amengual ◽  
Celso Garcia ◽  
Enrique Morán-Tejeda ◽  
Víctor Homar ◽  
...  
Keyword(s):  
Prediction Models ◽  
Extreme Event ◽  
Response Times ◽  
Flash Flood ◽  
Weather Prediction ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Management Service ◽  
Convective Rainfall ◽  
Hydraulic Simulation

Abstract. An extraordinary convective rainfall event, unforeseen by most numerical weather prediction models, generated a devastating flash flood (305 m3 s−1) in the town of Sant Llorenç des Cardassar, Mallorca, on 9 October 2018. Four people died inside this village, while casualties were up to 13 over the entire affected area. This extreme event has been reconstructed by implementing an integrated flash flood modelling approach in the Ses Planes catchment up to Sant Llorenç (23.4 km2), based on three components: (i) generation of radar-derived precipitation estimates, (ii) modelling of accurate discharge hydrographs yielded by the catchment (using FEST and KLEM models), and (iii) hydraulic simulation of the event and mapping of affected areas (using HEC-RAS). Radar-derived rainfall estimates show very high agreement with rain gauge data (R2=0.98). Modelled flooding extent is in close agreement with the observed extension by the Copernicus Emergency Management Service, based on Sentinel-1 imagery, and both far exceed the extension for a 500-year return period flood. Hydraulic simulation revealed that water reached a depth of 3 m at some points, and modelled water depths highly correlate (R2=0.91) with in situ after-event measurements. The 9 October flash flood eroded and transported woody and abundant sediment debris, changing channel geomorphology. Water velocity greatly increased at bridge locations crossing the river channel, especially at those closer to the Sant Llorenç town centre. This study highlights how the very low predictability of this type of extreme convective rainfall events and the very short hydrological response times typical of small Mediterranean catchments continue to challenge the implementation of early warning systems, which effectively reduce people's exposure to flash flood risk in the region.

scholarly journals On the Epochal Strengthening in the Relationship between Rainfall of East Africa and IOD

2013 ◽  
Vol 26 (15) ◽  
pp. 5655-5673 ◽  
Author(s):  
Desmond Manatsa ◽  
Swadhin K. Behera
Keyword(s):  
East Africa ◽  
Rainfall Variability ◽  
Spatial Coherence ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Global Precipitation Climatology Centre

Abstract Variability of the equatorial East Africa “short rains” (EASR) has intensified significantly since the turn of the twentieth century. This increase toward more extreme rainfall events has not been gradual but is strongly characterized by epochs. The rain gauge–based Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset for the period 1901–2009 is used to demonstrate that the epochal changes were dictated by shifts in the Indian Ocean dipole (IOD) mode. These shifts occurred during 1961 and 1997. In the pre-1961 period, there was virtually no significant linear link between the IOD and the EASR. But a relatively strong coupling between the two occurred abruptly in 1961 and was generally maintained at that level until 1997, when another sudden shift to even a greater level occurred. The first principal component (PC1) extracted from the EASR spatial domain initially merely explained about 50% of the rainfall variability before 1961, and then catapulted to about 73% for the period from 1961 to 1997, before eventually shifting to exceed 82% after 1997. The PC1 for each successive epoch also displayed loadings with notably improved spatial coherence. This systematic pattern of increase was accompanied by both a sharp increase in the frequency of rainfall extremes and spatial coherence of the rainfall events over the region. Therefore, it is most likely that the 1961 and 1997 IOD shifts are responsible for the epochal modulation of the EASR in both the spatial and temporal domain.

Heavy precipitating events in satellites and rain-gauge products over the Sahel

2021 ◽  
Author(s):  
Sidiki Sanogo ◽  
Philippe Peyrillé ◽  
Romain Roehrig ◽  
Françoise Guichard ◽  
Ousmane Ouedraogo
Keyword(s):  
Burkina Faso ◽  
West African Monsoon ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Extreme Rainfall Events ◽  
Microwave Sensor ◽  
Sahel Region ◽  
Distribution Intensity ◽  
Rain Gauge Network

<p>The Sahel has experienced an increase in the frequency and intensity of extreme rainfall events over the recent decades. These trends are expected to continue in the future. However the properties of these events have so far received little attention. In the present study, we define a heavy precipitating event (HPE) as the occurrence of daily-mean precipitation exceeding a given percentile (e.g., 99<sup>th</sup> and higher) over a 1°x1° pixel and examine their spatial distribution, intensity, seasonality and interannual variability. We take advantage of an original reference dataset based on a rather high-density rain-gauge network over Burkina Faso (142 stations) to evaluate 22 precipitation gridded datasets often used in the literature, based on rain-gauge-only measurements, satellite measurements, or both. Our reference dataset documents the HPEs over Burkina Faso. The 99<sup>th</sup> percentile identifies events greater than 26 mm d<sup>-1</sup> with a ~2.5 mm confidence interval depending on the number of stations within a 1°x1° pixel. The HPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. The evaluation of the gridded rainfall products reveals that only two of the datasets, namely the rain-gauge-only based products GPCC-DDv1 and REGENv1, are able to properly reproduce all of the HPE features examined in the present work. A subset of the remaining rainfall products also provide satisfying skills over Burkina Faso, but generally only for a few HPE features examined here. In particular, we notice a general better performance for rainfall products that include rain-gauge data in the calibration process, while estimates using microwave sensor measurements are prone to overestimate the HPE intensity. The agreement among the 22 datasets is also assessed over the entire Sahel region. While the meridional gradient in HPE properties is well captured by the good performance subset, the zonal direction exhibit larger inter-products spread. This advocates for the need to continue similar evaluation with the available rain-gauge network available in West Africa, both to enhance the HPE documentation and understanding at the scale of the region and to help improve the rainfall dataset quality.</p>

scholarly journals Extreme Rainfall in the Mediterranean: What Can We Learn from Observations?

2013 ◽  
Vol 14 (3) ◽  
pp. 906-922 ◽  
Author(s):  
N. Rebora ◽  
L. Molini ◽  
E. Casella ◽  
A. Comellas ◽  
E. Fiori ◽  
...  
Keyword(s):  
Extreme Rainfall ◽  
Flash Floods ◽  
Rain Gauge ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Life Threatening ◽  
The Mediterranean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ground Effects

Abstract Flash floods induced by extreme rainfall events represent one of the most life-threatening phenomena in the Mediterranean. While their catastrophic ground effects are well documented by postevent surveys, the extreme rainfall events that generate them are still difficult to observe properly. Being able to collect observations of such events will help scientists to better understand and model these phenomena. The recent flash floods that hit the Liguria region (Italy) between the end of October and beginning of November 2011 give us the opportunity to use the measurements available from a large number of sensors, both ground based and spaceborne, to characterize these events. In this paper, the authors analyze the role of the key ingredients (e.g., unstable air masses, moist low-level jets, steep orography, and a slow-evolving synoptic pattern) for severe rainfall processes over complex orography. For the two Ligurian events, this role has been analyzed through the available observations (e.g., Meteosat Second Generation, Moderate Resolution Imaging Spectroradiometer, the Italian Radar Network mosaic, and the Italian rain gauge network observations). The authors then address the possible role of sea–atmosphere interactions and propose a characterization of these events in terms of their predictability.

scholarly journals Evaluation of hillslope failure and community adjustment after extreme weather event, Waimarama 26-27 April, 2011

2021 ◽  
Author(s):  
◽  
Albert Edward Frampton
Keyword(s):  
Water Content ◽  
Small Businesses ◽  
Extreme Event ◽  
Extreme Rainfall ◽  
Field Measurements ◽  
Economic Loss ◽  
Laboratory Analysis ◽  
Annual Rainfall ◽  
Financial Loss ◽  
North West

<p>In 2011, Waimarama received 80% of its annual rainfall in 48 hours. This extreme event with a return period of >100 years caused saturated hillslopes to collapse forming 100s of shallow landslides in the Puhokio Valley. This study collected soil samples from 54 exposed slip scarp horizons for laboratory analysis of soil mechanical properties. Field measurements of slip and slope angles, length, width and depth to determine that 23,212m³ of sediment was volume lost, from the 54 landslides. The field and lab measurements were used to generate a coherent understanding of landsliding at Waimarama. Laboratory analysis for liquid limits water content showed a high of 88.5% to a low of 18.8% and plastic limit water content had a high of 51% in the A horizon (organics) and low of 16.1%. Specific gravity also indicated a high reading 1.74 g/cm³ with a low of 1.16 g/cm³. The A horizon was able to tolerate high levels of water content in most tests, while the B horizon was capable of coping with some increase in water content. The C horizon was only able to handle low volumes of water, and was the main initiator of regolith collapse. The laboratory results indicated high saturation levels within the horizons of weak lithology of marine regolith that over caps impervious marine bedrock. The main cause for hillslope collapse and exposure of multiple translational and debris flow landslides was extreme saturation. However, towards the height of the rainfall event a 4.5 magnitude earthquake was recorded with unknown collateral consequences. Most slip locations were found in the aspects of east, south-east, west, and north-west, and on slope angles 15 -25°. The study confirmed previous surveys that regolith depth 80-100cm on impervious sandstone, siltstone/mudstone, when saturated over lengthy wet spells or from extreme precipitation, will collapse. In addition to the physical geographic study a survey was included to record individual and family accounts of the weather phenomenon. A questionnaire was prepared with specific questions that required yes or no answers. These questions dealt with loss of buildings, loss of land, animals, financial loss and recovery, economic loss, insurance and mitigation plans. The most affected were farmers and the next affected were householders while the holiday park was the worst affected of small businesses. Insurance was a significant help in most recoveries. Land rehabilitation was mitigated with new plantings and some aerial sowing, otherwise many slips were left to revegetate naturally. Economic and financial loss was severe for most farmers, due to pasture loss and animal relocation. Extreme rainfall causes slips that affect humans, but they can be mitigated. The Waimarama event is one of many events that can happen countrywide, the results can be a disastrous loss of personal, economic and financial assets, loss of infrastructure, including roading, bridges and communication. These are factors that many people and communities only realise when it happens to them. Mitigation against such events might include adequate insurance and knowledge of what to do, and where to go should an event happen unexpectedly and without warning.</p>

Precipitation and Temperature Forecast Performance at the Weather Prediction Center

2014 ◽  
Vol 29 (3) ◽  
pp. 489-504 ◽  
Author(s):  
David R. Novak ◽  
Christopher Bailey ◽  
Keith F. Brill ◽  
Patrick Burke ◽  
Wallace A. Hogsett ◽  
...  
Keyword(s):  
Short Range ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Extreme Rainfall Events ◽  
Forecast Performance ◽  
The Past ◽  
Medium Range

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

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