Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

AbstractExtreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

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Identifying and Correcting Biases in Localized Downscaling Estimates of Daily Precipitation Return Values

10.21203/rs.3.rs-610939/v1 ◽

2021 ◽

Author(s):

Mark Risser ◽

Daniel Feldman ◽

Michael Wehner ◽

David Pierce ◽

Jeff Arnold

Keyword(s):

Observational Data ◽

Extreme Precipitation ◽

Daily Precipitation ◽

Time Of Day ◽

Economic Damage ◽

Data Set ◽

Extreme Precipitation Events ◽

Daily Precipitation Data ◽

Precipitation Events ◽

Made In

Abstract Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which down scaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the over all performance of LOCA-derived changes in extreme daily precipitation. We show that the low bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

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Annual and Seasonal Precipitation and Their Extremes over the Tibetan Plateau and Its Surroundings in 1963–2015

Atmosphere ◽

10.3390/atmos12050620 ◽

2021 ◽

Vol 12 (5) ◽

pp. 620

Author(s):

Jin Ding ◽

Lan Cuo ◽

Yongxin Zhang ◽

Cunjie Zhang ◽

Liqiao Liang ◽

...

Keyword(s):

Tibetan Plateau ◽

Extreme Precipitation ◽

Daily Precipitation ◽

The Tibetan Plateau ◽

Opposite Pattern ◽

Westerly Jet ◽

Extreme Precipitation Events ◽

Daily Precipitation Data ◽

Moisture Transports ◽

Precipitation Events

Based on daily precipitation data from 115 climate stations, seasonal and annual precipitation and their extremes over the Tibetan Plateau and its surroundings (TPS) in 1963–2015 are investigated. There exists a clear southeast-northwest gradient in precipitation and extreme daily precipitation but an opposite pattern for the consecutive dry days (CDDs). The wet southeast is trending dry while the dry center and northwest are trending wet in 1963–2015. Correspondingly, there is a drying tendency over the wet basins in the southeast and a wetting tendency over the dry and semi-dry basins in the center and northwest in summer, which will affect the water resources in the corresponding areas. The increase (decrease) in precipitation tends to correspond to the increase (decrease) in maximum daily precipitation but the decrease (increase) in CDDs. Extreme precipitation events with 20-year, 50-year, 100-year, and 200-year recurrence occurred frequently in the past decades especially in the 1980s. The greatest extreme precipitation events tend to occur after the late 1990s and in the southeastern TPS. The ERA5 reanalysis and climate system indices reveal that (1) decreased moisture transports to the southeast in summer due to the weakening of the summer monsoons and the East Asian westerly jet; (2) increased moisture transports to the center in winter due to the strengthening of the winter westerly jet and north Atlantic oscillation; and (3) decreased instability over the southeast thus suppressing precipitation and increased instability over the northwest thus promoting precipitation. All these are conducive to the drying trends in the southeast and the wetting trends in the center.

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ENSO and Wintertime Extreme Precipitation Events over the Contiguous United States

Journal of Climate ◽

10.1175/2007jcli1705.1 ◽

2008 ◽

Vol 21 (1) ◽

pp. 22-39 ◽

Cited By ~ 48

Author(s):

Siegfried D. Schubert ◽

Yehui Chang ◽

Max J. Suarez ◽

Philip J. Pegion

Keyword(s):

United States ◽

West Coast ◽

Extreme Precipitation ◽

East Coast ◽

Daily Precipitation ◽

Precipitation Variability ◽

The West ◽

Extreme Precipitation Events ◽

The Impact ◽

Precipitation Events

Abstract In this study the authors examine the impact of El Niño–Southern Oscillation (ENSO) on precipitation events over the continental United States using 49 winters (1949/50–1997/98) of daily precipitation observations and NCEP–NCAR reanalyses. The results are compared with those from an ensemble of nine atmospheric general circulation model (AGCM) simulations forced with observed SST for the same time period. Empirical orthogonal functions (EOFs) of the daily precipitation fields together with compositing techniques are used to identify and characterize the weather systems that dominate the winter precipitation variability. The time series of the principal components (PCs) associated with the leading EOFs are analyzed using generalized extreme value (GEV) distributions to quantify the impact of ENSO on the intensity of extreme precipitation events. The six leading EOFs of the observations are associated with major winter storm systems and account for more than 50% of the daily precipitation variability along the West Coast and over much of the eastern part of the country. Two of the leading EOFs (designated GC for Gulf Coast and EC for East Coast) together represent cyclones that develop in the Gulf of Mexico and occasionally move and/or redevelop along the East Coast producing large amounts of precipitation over much of the southern and eastern United States. Three of the leading EOFs represent storms that hit different sections of the West Coast (designated SW for Southwest coast, WC for the central West Coast, and NW for northwest coast), while another represents storms that affect the Midwest (designated by MW). The winter maxima of several of the leading PCs are significantly impacted by ENSO such that extreme GC, EC, and SW storms that occur on average only once every 20 years (20-yr storms) would occur on average in half that time under sustained El Niño conditions. In contrast, under La Niña conditions, 20-yr GC and EC storms would occur on average about once in 30 years, while there is little impact of La Niña on the intensity of the SW storms. The leading EOFs from the model simulations and their connections to ENSO are for the most part quite realistic. The model, in particular, does very well in simulating the impact of ENSO on the intensity of EC and GC storms. The main model discrepancies are the lack of SW storms and an overall underestimate of the daily precipitation variance.

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Features of Extreme Precipitation at Progress Station, Antarctica

Journal of Climate ◽

10.1175/jcli-d-18-0128.1 ◽

2018 ◽

Vol 31 (22) ◽

pp. 9087-9105 ◽

Cited By ~ 5

Author(s):

Lejiang Yu ◽

Qinghua Yang ◽

Timo Vihma ◽

Svetlana Jagovkina ◽

Jiping Liu ◽

...

Keyword(s):

Water Vapor ◽

Extreme Precipitation ◽

Dynamic Processes ◽

Annual Number ◽

Positive Trend ◽

Self Organizing Maps ◽

Dipole Structure ◽

Extreme Precipitation Events ◽

Daily Precipitation Data ◽

Precipitation Events

Observed daily precipitation data were used to investigate the characteristics of precipitation at Antarctic Progress Station and synoptic patterns associated with extreme precipitation events during the period 2003–16. The annual precipitation, annual number of extreme precipitation events, and amount of precipitation during the extreme events have positive trends. The distribution of precipitation at Progress Station is heavily skewed with a long tail of extreme dry days and a high peak of extreme wet days. The synoptic pattern associated with extreme precipitation events is a dipole structure of negative and positive height anomalies to the west and east of Progress Station, respectively, resulting in water vapor advection to the station. For the first time, we apply self-organizing maps (SOMs) to examine thermodynamic and dynamic perspectives of trends in the frequency of occurrence of Antarctic extreme precipitation events. The changes in thermodynamic (noncirculation) processes explain 80% of the trend, followed by the changes in the interaction between thermodynamic and dynamic processes, which account for nearly 25% of the trend. The changes in dynamic processes make a negative (less than 5%) contribution to the trend. The positive trend in total column water vapor over the Southern Ocean explains the change of thermodynamic term.

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A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

10.5194/egusphere-egu21-836 ◽

2021 ◽

Author(s):

Jérôme Kopp ◽

Pauline Rivoire ◽

S. Mubashshir Ali ◽

Yannick Barton ◽

Olivia Martius

Keyword(s):

Time Scales ◽

Extreme Precipitation ◽

Large Scale ◽

Time Window ◽

Heat Waves ◽

Reanalysis Data ◽

Data Set ◽

Temporal Clustering ◽

Extreme Precipitation Events ◽

Precipitation Events

Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 &#8211; 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.

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Impacts of Atmospheric Rivers in Extreme Precipitation on the European Macaronesian Islands

Atmosphere ◽

10.3390/atmos9080325 ◽

2018 ◽

Vol 9 (8) ◽

pp. 325 ◽

Cited By ~ 3

Author(s):

Alexandre M. Ramos ◽

Ricardo M. Trigo ◽

Ricardo Tomé ◽

Margarida L. R. Liberato

Keyword(s):

Canary Islands ◽

Extreme Precipitation ◽

Daily Precipitation ◽

Comprehensive Assessment ◽

Atmospheric Rivers ◽

Extreme Precipitation Events ◽

Macaronesian Islands ◽

Weather Stations ◽

The Relationship ◽

Precipitation Events

The European Macaronesia Archipelagos (Azores, Madeira and Canary Islands) are struck frequently by extreme precipitation events. Here we present a comprehensive assessment on the relationship between atmospheric rivers and extreme precipitation events in these three Atlantic Archipelagos. The relationship between the daily precipitation from the various weather stations located in the different Macaronesia islands and the occurrence of atmospheric rivers (obtained from four different reanalyses datasets) are analysed. It is shown that the atmospheric rivers’ influence over extreme precipitation (above the 90th percentile) is higher in the Azores islands when compared to Madeira or Canary Islands. In Azores, for the most extreme precipitation days, the presence of atmospheric rivers is particularly significant (up to 50%), while for Madeira, the importance of the atmospheric rivers is reduced (between 30% and 40%). For the Canary Islands, the occurrence of atmospheric rivers on extreme precipitation is even lower.

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Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0147.1 ◽

2015 ◽

Vol 16 (6) ◽

pp. 2537-2557 ◽

Cited By ~ 46

Author(s):

Laurie Agel ◽

Mathew Barlow ◽

Jian-Hua Qian ◽

Frank Colby ◽

Ellen Douglas ◽

...

Keyword(s):

Extreme Precipitation ◽

Seasonal Cycle ◽

Large Scale ◽

Daily Precipitation ◽

Warm Season ◽

Extreme Precipitation Events ◽

Tail Distribution ◽

Hourly Data ◽

Early Fall ◽

Precipitation Events

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

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Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Water ◽

10.3390/w12010193 ◽

2020 ◽

Vol 12 (1) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Chaoxing Sun ◽

Guohe Huang ◽

Yurui Fan

Keyword(s):

Loess Plateau ◽

Return Period ◽

Extreme Precipitation ◽

Precipitation Amount ◽

Heavy Precipitation ◽

The Loess Plateau ◽

Extreme Precipitation Events ◽

Daily Precipitation Data ◽

Joint Return ◽

Precipitation Events

The unique characteristics of topography, landforms, and climate in the Loess Plateau make it especially important to investigate its extreme precipitation characteristics. Daily precipitation data of Loess Plateau covering a period of 1959–2017 are applied to evaluate the probability features of five precipitation indicators: the amount of extreme heavy precipitation (P95), the days with extreme heavy precipitation, the intensity of extreme heavy precipitation (I95), the continuous dry days, and the annual total precipitation. In addition, the joint risk of different combinations of precipitation indices is quantitatively evaluated based on the copula method. Moreover, the risk and severity of each extreme heavy precipitation factor corresponding to 50-year joint return period are achieved through inverse derivation process. Results show that the precipitation amount and intensity of the Loess Plateau vary greatly in spatial distribution. The annual precipitation in the northwest region may be too concentrated in several rainstorms, which makes the region in a serious drought state for most of the year. At the level of 10-year return period, more than five months with no precipitation events would occur in the Northwest Loess Plateau. While, P95 or I95 events of 100-year level may be encountered in a 50-year return period and in the southeastern region, which means there are foreseeable long-term extreme heavy precipitation events.

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Scale-dependent representation of extreme precipitation processes in regional and CPM scale simulations for the greater Alpine region

10.5194/egusphere-egu21-4852 ◽

2021 ◽

Author(s):

Alberto Caldas-Alvarez ◽

Hendrik Feldmann ◽

Joaquim G. Pinto

Keyword(s):

Extreme Precipitation ◽

Rare Events ◽

Potential Temperature ◽

Low Pressure ◽

Scale Dependency ◽

Data Set ◽

Extreme Precipitation Events ◽

Spatial Coverage ◽

Precipitation Processes ◽

Precipitation Events

Extreme precipitation events with return periods above 100-years (Most Extreme Precipitation Events; MEPE) are rare events by definition, as the observational record covers very few of such events. Therefore, our knowledge is insufficient to assess their potential intensities and physical processes on different scales. To fill this gap, large regional climate ensembles, like the one provided by the German Decadal Climate Predictions (MiKlip) project (> 10.000 years), are of great value as they provide a larger sample size of such rare events. The RCM ensemble samples present day climate conditions multiple times (Ehmele et al., 2020) with a resolution of 25 km, and thus it does not resolve the convection permitting scales (CPM).In this study, we aim to combine the large RCM ensemble with episodic CPM-scale downscaling simulations to derive a better statistical and process related representation of MEPEs for Central Europe. As a first step, we evaluate two re-analysis driven long-term simulations with COSMO-CLM (CCLM) from MiKlip and CORDEX-FPS Convection with respect to their scale-dependent representation.The simulations span the period 1971 to 2016 with the 25 km simulation and are forced by ERA40 until 1979 and by ERA-interim afterwards. The CPM simulation (~3 km) is forced by ERA-40 between 1971 and 1999 and by ERA-interim between 2000 and 2016. We validate the simulations against E-OBS (25 km) and the unique HYdrologische RASterdatens&#228;tze (HYRAS) precipitation data set (5 km). The investigation area is the greater Alpine area. We employ a Precipitation Severity Index (PSI) adapted from extreme wind detection (Leckebusch et al., 2008; Pinto et al., 2012) for extreme precipitation cases. The advantage of the PSI is its ability to account for extreme grid point precipitation as well as spatial coverage and event duration. The events are categorized objectively into composite Weather Types (WT) to enable further generalization of the findings.The results show a clear overestimation of precipitation for the analysed period and area by the RCMs at both resolutions. However, large differences exist the representation of extreme precipitation. Compared to observations, the 3 km (25km) resolution overestimates (underestimates) precipitation intensity for extreme cases. This agrees with previous literature. Five different WTs are identified for the analysed period, with Autumn-Winter WT being the most common, followed by convective summer WT. The Autumn-Winter WT is characterized by deep, cold, low-pressure areas located over Northern Europe. Summer WT cases are characterized by stable high-pressure situations affected by incurring small low-pressure systems on its western flank (convective-prone situations). Regarding the scale dependency of precipitation processes, the coarse resolution tends to overestimate surface moisture in situations of heavy precipitation, leading to larger latent instability (CAPE) in the 25 km resolution than in its 3 km counterpart. Furthermore, a large-scale dependency is found in summer extreme precipitation cases for two stability-related variables, Equivalent Potential Temperature (&#952;e850) at 850 hPa and moisture flux at the Lower Free Troposphere (LFT-moisture). In these cases, the overestimation (underestimation) of &#160;and LFT-moisture by either resolution is in line with their precipitation overestimation (underestimation).

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Assessing the Use of Satellite-Based Estimates and High-Resolution Precipitation Datasets for the Study of Extreme Precipitation Events over the Iberian Peninsula

Water ◽

10.3390/w10111688 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1688 ◽

Cited By ~ 6

Author(s):

Riccardo Hénin ◽

Margarida Liberato ◽

Alexandre Ramos ◽

Célia Gouveia

Keyword(s):

High Resolution ◽

Iberian Peninsula ◽

Real Time ◽

Extreme Precipitation ◽

Daily Precipitation ◽

Weather Forecast ◽

Winter Period ◽

Medium Range Weather Forecast ◽

Extreme Precipitation Events ◽

Precipitation Events

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.

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