Representation of Precipitation Characteristics and Extremes in Regional Reanalyses and Satellite- and Gauge-Based Estimates over Western and Central Europe

Abstract This paper evaluates several daily precipitation products over western and central Europe, identifies and documents their respective strengths and shortcomings, and relates these to uncertainties associated with each of the products. We analyze one gauge-based, three satellite-based, and two reanalysis-based products using high-density rain gauge observations as reference. First, we assess spatial patterns and frequency distributions using aggregated statistics. Then, we determine the skill of precipitation event detection from these products with a focus on extremes, using temporally and spatially matched pairs of precipitation estimates. The results show that the quality of the datasets largely depends on the region, season, and precipitation characteristic addressed. The satellite and the reanalysis precipitation products are found to have difficulties in accurately representing precipitation frequency with local overestimations of more than 40%, which occur mostly in dry regions (all products) as well as along coastlines and over cold/frozen surfaces (satellite-based products). The frequency distributions of wet-day intensities are generally well reproduced by all products. Concerning the frequency distributions of wet-spell durations, the satellite-based products are found to have clear deficiencies for maritime-influenced precipitation regimes. Moreover, the analysis of the detection of extreme precipitation events reveals that none of the non-station-based datasets shows skill at the shortest temporal and spatial scales (1 day, 0.25°), but at and above the 3-day and 1.25° scale the products start to exhibit skill over large parts of the domain. Added value compared to coarser-resolution global benchmark products is found both for reanalysis and satellite-based products.

Download Full-text

Evaluating the skill of satellite data on the individuation of extreme precipitation events in Calabria (southern Italy)

10.5194/egusphere-egu2020-3071 ◽

2020 ◽

Author(s):

Tommaso Caloiero ◽

Roberto Coscarelli ◽

Giulio Nils Caroletti

Keyword(s):

Satellite Data ◽

Extreme Precipitation ◽

Southern Italy ◽

Hot Spot ◽

Rain Gauge ◽

Precipitation Event ◽

The European Union ◽

Extreme Precipitation Events ◽

Daily Data ◽

Precipitation Events

In this study, the skill of TRMM Multi-Satellite Precipitation Analysis (TMPA) data to locate spatially and temporally extreme precipitation has been tested over Calabria, a region in southern Italy.Calabria is a very challenging region for hydrometeorology studies, as i) it is a mainly mountainous region with complex orography; ii) it is surrounded by sea, providing&#160; an abundance of available moisture; iii) it belongs to the Mediterranean region, a hot-spot for climate change.TMPA, which provides daily data at a 0.25&#176; resolution (i.e., about 25 km at southern Italy latitudes), was tested with regards to three extreme precipitation events that occurred between 1998 and 2019, i.e., the years of TMPA&#8217;s operational time frame. The first event, taking place on 07-12/09/2000, lasted for several days and involved most of Calabria. The second (01-04/07/2006) was a very localized midsummer event, which hit a very small area with destructive consequences. Finally, the 18-27/11/2013 event was a ten-day long heavy precipitation event that hit the region in spots.TMPA daily data were compared against validated and homogenized rain gauge data from 79 stations managed by the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection. TMPA was evaluated both in relative and absolute terms: i) the relative skill was tested by checking if TMPA evaluated correctly the presence of extreme precipitation, defined as daily precipitation passing the 99th percentile threshold; ii) the absolute skill was tested by checking if TMPA reproduced correctly the cumulated precipitation values during the events.TMPA proved sufficiently able to locate areas subject to heavy cumulated precipitation during large spatially distributed events over the region. However, it showed difficulties in reproducing very localized events, as the 2006 case study was not detected at all, showing that 25-km spatial resolution and daily time resolution proved inadequate to resolve this type of rainfall event.Results might give insights into the possibility of using satellite data for real-time monitoring of extreme precipitation, especially since the transition from the old TMPA to the new Integrated Multi-satellitE Retrievals for GPM (IMERG) set was completed in January 2020.&#160;Acknowledgments:The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

Download Full-text

Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0144.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1425-1451 ◽

Cited By ~ 4

Author(s):

Camille Birman ◽

Fatima Karbou ◽

Jean-François Mahfouf ◽

Matthieu Lafaysse ◽

Yves Durand ◽

...

Keyword(s):

Snow Depth ◽

Positive Impact ◽

Weather Prediction ◽

Rain Gauge ◽

Added Value ◽

Background Information ◽

Time Step ◽

Rain Gauges ◽

Weather Radars ◽

Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

Download Full-text

Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales

Journal of Climate ◽

10.1175/jcli-d-15-0618.1 ◽

2016 ◽

Vol 29 (21) ◽

pp. 7773-7795 ◽

Cited By ~ 59

Author(s):

Maria Gehne ◽

Thomas M. Hamill ◽

George N. Kiladis ◽

Kevin E. Trenberth

Keyword(s):

Time Scales ◽

Large Scale ◽

Global Climate ◽

Spatial Scales ◽

Rain Gauge ◽

Random Errors ◽

Climate Data ◽

Precipitation Estimates ◽

Global Precipitation ◽

Daily Time

Abstract Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.

Download Full-text

Evaluation of High-Resolution Satellite-Based Real-Time and Post-Real-Time Precipitation Estimates during 2010 Extreme Flood Event in Swat River Basin, Hindukush Region

Advances in Meteorology ◽

10.1155/2016/2604980 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 21

Author(s):

Muhammad Naveed Anjum ◽

Yongjian Ding ◽

Donghui Shangguan ◽

Muhammad Wajid Ijaz ◽

Shiqiang Zhang

Keyword(s):

Real Time ◽

Correlation Coefficient ◽

Spatial Correlation ◽

River Basin ◽

Heavy Precipitation ◽

Precipitation Event ◽

Extreme Precipitation Events ◽

Extreme Flood ◽

Precipitation Estimates ◽

Swat River

Satellite-based real-time and post-real-time precipitation estimates of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) were evaluated during an extreme heavy precipitation event (on 28–30 July 2010) over Swat River Basin and adjacent areas in Hindukush Region. Observations of 15 rain gauging stations were used for the evaluation of TMPA products. Results showed that the spatial pattern of precipitation in the event was generally captured by post-real-time product (3B42V7) but misplaced by real-time product (3B42RT), witnessed by a high spatial correlation coefficient for 3B42V7 (CC = 0.87) and low spatial correlation coefficient for 3B42RT (CC = 0.20). The temporal variation of the storm precipitation was not well captured by both TMPA products. 3B42V7 product underestimated the storm accumulated precipitation by 32.15%, while underestimation by 3B42RT was 66.73%. Based on the findings of this study, we suggest that the latest TMPA-based precipitation products, 3B42RT and 3B42V7, might not be able to perform well during extreme precipitation events, particularly in complex terrain regions like Hindukush Mountains. Therefore, cautions should be considered while using 3B42RT and 3B42V7 as input data source for the modelling, forecasting, and monitoring of floods and potential landslides in Hindukush Region.

Download Full-text

Temperature scaling of extreme precipitation events – an application of the new DWD event catalogue

10.5194/ems2021-452 ◽

2021 ◽

Author(s):

Tanja Winterrath ◽

Ewelina Walawender ◽

Katharina Lengfeld ◽

Elmar Weigl ◽

Andreas Becker

Keyword(s):

Extreme Precipitation ◽

Ambient Air ◽

Precipitation Intensity ◽

Convective Precipitation ◽

Precipitation Event ◽

Extreme Precipitation Events ◽

Precipitation Regimes ◽

Temperature Scaling ◽

First Results ◽

Precipitation Events

According to the Clausius-Clapeyron equation on saturation vapour pressure a temperature increase of 1&#160;K allows an atmospheric air mass to hold approximately 7&#160;% more water vapour thus increasing its potential for heavy precipitation. Several published measurement studies on the relation between precipitation intensity and temperature, however, revealed an increase of even up to twofold the CC rate for short-term precipitation events. Model conceptions explain this scaling behaviour with increasing temperature by different intensification pathways of convective processes and/or a transition between stratiform and convective precipitation regimes that both can hardly be verified by point measurements alone. In this presentation, we present first results of the correlation between ambient air temperature and different attributes of the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE) recently published by Deutscher Wetterdienst (DWD). This object-oriented event catalogue files and characterizes extreme precipitation events that have occurred on German territory since 2001. It is based on the high-resolution precipitation climate data set RADKLIM of DWD, i.e. contiguous radar-based reflectivity measurements adjusted to hourly station-based precipitation totals and corrected for typical measurement errors applying specific climatological correction methods. Our analysis gives new insights into potential explanations of the observed temperature scaling relating not only precipitation intensity but characteristic event properties like area, duration, and extremity indices with ambient temperature data. With this approach, extreme precipitation events can be analysed in a comprehensive way that is significant in the context of potential impact. The presented analysis moreover allows testing the hypothesis of regime changing based on objective precipitation event criteria that are typical for different precipitation types. We will briefly present the methodological background of CatRaRE with special focus on the event attributes used in the analysis of Clausius-Clapeyron scaling and give first results on the retrieved temperature dependencies of extreme precipitation events.

Download Full-text

Extreme rainfall drives early onset cyanobacterial bloom

10.1101/570275 ◽

2019 ◽

Author(s):

Megan L. Larsen ◽

Helen M. Baulch ◽

Sherry L. Schiff ◽

Dana F. Simon ◽

Sébastien Sauvé ◽

...

Keyword(s):

Extreme Precipitation ◽

Algal Blooms ◽

Nutrient Loading ◽

Climate Models ◽

Cyanobacterial Bloom ◽

Extreme Rainfall ◽

Precipitation Event ◽

List Type ◽

Precipitation Frequency ◽

Extreme Precipitation Events

AbstractThe increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production.We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom, successional progression of the phytoplankton community, toxins, and geochemistry.An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks post flood and ~5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks.Synthesis and applications: These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure. In contrast to the effects of early season extreme rainfall, a mid-summer runoff event appeared to help mitigate the bloom in some areas of the reservoir by increasing flushing.

Download Full-text

Ground validation of GPM IMERG-F precipitation products with the point rain gauge records on the extreme rainfall over a mountainous area of Sumatra Island

Jurnal Penelitian Pendidikan IPA ◽

10.29303/jppipa.v8i1.1155 ◽

2022 ◽

Vol 8 (1) ◽

pp. 163-170

Author(s):

Ravidho Ramadhan ◽

Marzuki Marzuki ◽

Helmi Yusnaini ◽

Ayu Putri Ningsih ◽

Hiroyuki Hashiguchi ◽

...

Keyword(s):

Precipitation Amount ◽

Extreme Rainfall ◽

Rain Gauge ◽

Precipitation Intensity ◽

Surface Measurement ◽

Mountainous Area ◽

Precipitation Frequency ◽

Sumatra Island ◽

Ground Validation ◽

Precipitation Estimates

Accurate satellite precipitation estimates over areas of complex topography are still challenging, while such accuracy is of importance to the adoption of satellite data for hydrological applications. This study evaluated the ability of Integrated Multi-satellitE Retrievals for GPM -Final (IMERG) V06 product to observe the extreme rainfall over a mountainous area of Sumatra Island. Fifteen years of optical rain gauge (ORG) observation at Kototabang, West Sumatra, Indonesia (100.32°E, 0.20°S, 865 m above sea level), were used as reference surface measurement. The performance of IMERG-F was evaluated using 13 extreme rain indexes formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI). The IMERG-F overestimated the values of all precipitation amount-based indices (PRCPTOT, R85P, R95P, and R99P), three precipitation frequency-based indices (R1mm, R10mm, R20mm), one precipitation duration-based indices (CWD), and one precipitation intensity-based indices (RX5day). Furthermore, the IMERG-F underestimated the values of precipitation frequency-based indices (R50mm), one precipitation duration-based indices (CDD), one precipitation intensity-based indices (SDII). In terms of correlation, only five indexes have a correlation coefficient (R) > 0.5, consistent with Kling–Gupta Efficiency (KGE) value. These results confirm the need to improve the accuracy of the IMERG-F data in mountainous areas.

Download Full-text

A Comparative Analysis of the Historical Accuracy of the Point Precipitation Frequency Estimates of Four Data Sets and Their Projections for the Northeastern United States

Water ◽

10.3390/w11061279 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1279 ◽

Cited By ~ 2

Author(s):

Shu Wu ◽

Momcilo Markus ◽

David Lorenz ◽

James Angel ◽

Kevin Grady

Keyword(s):

United States ◽

Rain Gauge ◽

Representative Concentration Pathway ◽

Exceedance Probability ◽

Data Sets ◽

Northeastern United States ◽

Precipitation Frequency ◽

Data Set ◽

Extreme Precipitation Events ◽

Historical Accuracy

Many studies have projected that as the climate changes, the magnitudes of extreme precipitation events in the Northeastern United States are likely to continue increasing, regardless of the emission scenario. To examine this issue, we analyzed observed and modeled daily precipitation frequency (PF) estimates in the Northeastern US on the rain gauge station scale based on both annual maximum series (AMS) and partial duration series (PDS) methods. We employed four Coupled Model Intercomparison Project Phase 5 (CMIP5) downscaled data sets, including a probabilistic statistically downscaled data set developed specifically for this study. The ability of these four data sets to reproduce the observed features of historical point PF estimates was compared, and the two with the best historical accuracy, including the newly developed probabilistic data set, were selected to produce projected PF estimates under two CMIP5-based emission scenarios, namely Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5). These projections indeed demonstrate a likely increase in PF estimates in the Northeastern US with noted differences in magnitudes and spatial distributions between the two data sets and between the two scenarios. We also quantified how the exceedance probabilities of the historical PF estimate values are likely to increase under each scenario using the two best performing data sets. Notably, an event with a current exceedance probability of 0.01 (a 100-year event) may have an exceedance probability for the second half of the 21st century of ≈0.04 (a 27-year event) under the RCP4.5 scenario and ≈0.05 (a 19-year event) under RCP8.5. Knowledge about the projected changes to the magnitude and frequency of heavy precipitation in this region will be relevant for the socio-economic and environmental evaluation of future infrastructure projects and will allow for better management and planning decisions.

Download Full-text

Numerical Simulation of a Heavy Precipitation Event in the Vicinity of Madrid-Barajas International Airport: Sensitivity to Initial Conditions, Domain Resolution, and Microphysics Parameterizations

Atmosphere ◽

10.3390/atmos9090329 ◽

2018 ◽

Vol 9 (9) ◽

pp. 329 ◽

Cited By ~ 8

Author(s):

Pedro Bolgiani ◽

Sergio Fernández-González ◽

Francisco Valero ◽

Andrés Merino ◽

Eduardo García-Ortega ◽

...

Keyword(s):

Initial Conditions ◽

Deep Convection ◽

Wrf Model ◽

Heavy Precipitation ◽

Early Warning Systems ◽

Rain Gauge ◽

Precipitation Event ◽

Temporal Behaviour ◽

Precipitation Estimates ◽

Using Data

Deep convection is a threat to many human activities, with a great impact on aviation safety. On 7 July 2017, a widespread torrential precipitation event (associated with a cut-off low at mid-levels) was registered in the vicinity of Madrid, causing serious flight disruptions. During this type of episode, accurate short-term forecasts are key to minimizing risks to aviation. The aim of this research is to improve early warning systems by obtaining the best WRF model setup. In this paper, the aforementioned event was simulated. Various model configurations were produced using four different physics parameterizations, 3-km and 1-km domain resolutions, and 0.25° and 1° initial condition resolutions. Simulations were validated using data from 17 rain gauge stations. Two validation indices are proposed, accounting for the temporal behaviour of the model. Results show significant differences between microphysics parameterizations. Validation of domain resolution shows that improvement from 3 to 1 km is negligible. Interestingly, the 0.25° resolution for initial conditions produced poor results compared with 1°. This may be linked to a timing error, because precipitation was simulated further east than observed. The use of ensembles generated by combining different WRF model configurations produced reliable precipitation estimates.

Download Full-text

Assesment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of Iberian Peninsula

10.20944/preprints201805.0150.v1 ◽

2018 ◽

Author(s):

Fulgencio Cánovas-García ◽

Sandra García-Galiano ◽

Francisco Alonso-Sarría

Keyword(s):

Iberian Peninsula ◽

Real Time ◽

Extreme Rainfall ◽

Rain Gauge ◽

Real Time Monitoring ◽

Rain Gauges ◽

Extreme Precipitation Events ◽

Empirical Calibration ◽

Precipitation Estimates ◽

Quantitative Precipitation Estimates

QPEs (Quantitative Precipitation Estimates) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of two QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS, a satellite-based QPE. The second is a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that both QPEs were well below the rain gauge values, especially in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. It does not seem that radar is more accurate than PERSIANN-CCS, despite its larger spatial resolution and its commonly higher effectiveness. The main conclusion is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

Download Full-text