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Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Significance Statement
The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.
Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Significance Statement
The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.
Abstract
Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.
Abstract
Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.
Abstract
IMERG provides state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near-real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a 5-yr period (2016–20). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products, i.e., MRMS, IMERG-Early, and IMERG-Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric Kling–Gupta efficiency (KGE), in streamflow prediction regarding basin scales ranging from 10 to 30 000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to the IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.
Abstract
IMERG provides state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near-real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a 5-yr period (2016–20). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products, i.e., MRMS, IMERG-Early, and IMERG-Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric Kling–Gupta efficiency (KGE), in streamflow prediction regarding basin scales ranging from 10 to 30 000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to the IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.
Abstract
Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm.
Significance Statement
Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
Abstract
Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm.
Significance Statement
Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
Abstract
In partial plastic-mulch-covered croplands, the complicated coexistence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo αb , mulched soil surface albedo αm , and canopy surface albedo (αc ) during the whole growth period. To accurately estimate α, αb , αm , and αc , and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb , Fm , Fc ), 1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α and 2) dynamic variations of αb , αm , αc and Fb , Fm , Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, and αc was determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb , αm , and αc that was established in this study. Results indicated that 1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when the ground surface is not fully covered by the crop canopy, while not underestimating α by a mean value of 0.061 in the early growth period, and 2) mean values of α, αb , αm , and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb , Fm , and Fc were 0.08, 0.42, and 0.50, respectively.
Abstract
In partial plastic-mulch-covered croplands, the complicated coexistence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo αb , mulched soil surface albedo αm , and canopy surface albedo (αc ) during the whole growth period. To accurately estimate α, αb , αm , and αc , and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb , Fm , Fc ), 1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α and 2) dynamic variations of αb , αm , αc and Fb , Fm , Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, and αc was determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb , αm , and αc that was established in this study. Results indicated that 1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when the ground surface is not fully covered by the crop canopy, while not underestimating α by a mean value of 0.061 in the early growth period, and 2) mean values of α, αb , αm , and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb , Fm , and Fc were 0.08, 0.42, and 0.50, respectively.
Abstract
The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.
Abstract
The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.
Abstract
Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.
Abstract
Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.
Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain-gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental US across a set of widely-used QPE products. We examine five datasets over an 18-year span (2002-2019). The products include three satellite-based products, CPC MORPHing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission - Multisatellite Precipitation Analysis (TRMM-TMPA), the ground-radar and rain-gauge-based NCEP Stage IV, and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans and Houston metropolitan areas. Along the East Coast, TC can contribute up to 20% of the warm-season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that the Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM and ERA5. As a result, satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty is further reflected in the TC flooding potential measured by the Extreme Rain Multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced compared to the NCEP Stage IV.
Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain-gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental US across a set of widely-used QPE products. We examine five datasets over an 18-year span (2002-2019). The products include three satellite-based products, CPC MORPHing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission - Multisatellite Precipitation Analysis (TRMM-TMPA), the ground-radar and rain-gauge-based NCEP Stage IV, and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans and Houston metropolitan areas. Along the East Coast, TC can contribute up to 20% of the warm-season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that the Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM and ERA5. As a result, satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty is further reflected in the TC flooding potential measured by the Extreme Rain Multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced compared to the NCEP Stage IV.
Abstract
A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (∼25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (∼15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30–40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
Abstract
A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (∼25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (∼15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30–40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
