Pub Date : 2023-06-22DOI: 10.1175/jamc-d-23-0009.1
M. Lindskog, R. Azad, S. de Haan, Jesper Blomster, M. Ridal
MetCoOp is a northern European collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states of this model are produced using a 3-dimensional variational data assimilation scheme utilizing a large number of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Centre, are used to derive indirect information of atmospheric wind-speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations that was encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.
{"title":"Impact of Mode-S Enhanced Surveillance Weather Observations on Weather Forecasts over the MetCoOp Northern European model domain","authors":"M. Lindskog, R. Azad, S. de Haan, Jesper Blomster, M. Ridal","doi":"10.1175/jamc-d-23-0009.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0009.1","url":null,"abstract":"\u0000MetCoOp is a northern European collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states of this model are produced using a 3-dimensional variational data assimilation scheme utilizing a large number of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Centre, are used to derive indirect information of atmospheric wind-speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations that was encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":"43 1-3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41306799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-21DOI: 10.1175/jamc-d-22-0147.1
Julia F. Lockwood, N. Dunstone, L. Hermanson, G. Saville, Adam A. Scaife, Doug M. Smith, H. Thornton
North Atlantic hurricane activity exhibits significant variation on multi-annual timescales. Advance knowledge of periods of high activity would be beneficial to the insurance industry, as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skilful at predicting North Atlantic hurricane activity averaged over periods of 2-10 years. We show that this skill also translates into skilful predictions of real-world US hurricane damages. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-year-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-year-total US hurricane damages (given in US dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and US damages are then used to produce probabilistic forecasts. The predictions of hurricane activity and US damages for the coming period 2020-2024 are high, with ~95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skilful.
{"title":"A decadal climate service for insurance: Skilful multi-year predictions of North Atlantic hurricane activity and US hurricane damage","authors":"Julia F. Lockwood, N. Dunstone, L. Hermanson, G. Saville, Adam A. Scaife, Doug M. Smith, H. Thornton","doi":"10.1175/jamc-d-22-0147.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0147.1","url":null,"abstract":"\u0000North Atlantic hurricane activity exhibits significant variation on multi-annual timescales. Advance knowledge of periods of high activity would be beneficial to the insurance industry, as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skilful at predicting North Atlantic hurricane activity averaged over periods of 2-10 years. We show that this skill also translates into skilful predictions of real-world US hurricane damages. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-year-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-year-total US hurricane damages (given in US dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and US damages are then used to produce probabilistic forecasts. The predictions of hurricane activity and US damages for the coming period 2020-2024 are high, with ~95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skilful.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44702100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.1175/jamc-d-23-0004.1
Yuekui Yang, Daniel Kiv, Surendra Bhatta, M. Ganeshan, Xiaomei Lu, S. Palm
This paper presents the work on using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern Era Retrospective analysis for Research and Applications v2 (MERRA-2) data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2m level, and wind speed at the 10m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using the year 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.
{"title":"Diagnosis of Antarctic Blowing Snow Properties Using MERRA-2 Reanalysis with a Machine Learning Model","authors":"Yuekui Yang, Daniel Kiv, Surendra Bhatta, M. Ganeshan, Xiaomei Lu, S. Palm","doi":"10.1175/jamc-d-23-0004.1","DOIUrl":"https://doi.org/10.1175/jamc-d-23-0004.1","url":null,"abstract":"\u0000This paper presents the work on using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern Era Retrospective analysis for Research and Applications v2 (MERRA-2) data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2m level, and wind speed at the 10m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using the year 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42784473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1175/jamc-d-22-0179.1
V. Petković, P. Brown, W. Berg, D. Randel, Spencer R. Jones, C. Kummerow
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently-produced global precipitation climate data records. Generated from inter-calibrated observations collected by a constellation of Passive Microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s Dual-frequency Precipitation Radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in Tropics can exceed 10%.
{"title":"Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?","authors":"V. Petković, P. Brown, W. Berg, D. Randel, Spencer R. Jones, C. Kummerow","doi":"10.1175/jamc-d-22-0179.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0179.1","url":null,"abstract":"\u0000Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently-produced global precipitation climate data records. Generated from inter-calibrated observations collected by a constellation of Passive Microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s Dual-frequency Precipitation Radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in Tropics can exceed 10%.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46422366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-12DOI: 10.1175/jamc-d-22-0131.1
D. Xi, N. Lin, Norberto C. Nadal-Caraballo, Madison C. Yawn
In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly-proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in continental U. S.; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.
{"title":"Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model","authors":"D. Xi, N. Lin, Norberto C. Nadal-Caraballo, Madison C. Yawn","doi":"10.1175/jamc-d-22-0131.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0131.1","url":null,"abstract":"In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly-proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in continental U. S.; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45591182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-07DOI: 10.1175/jamc-d-22-0156.1
Brian N. Belcher, A. Degaetano, F. Masters, J. Crandell, Murray J. Morrison
A method is presented to obtain the climatology of extreme wind speeds coincident with the occurrence of rain. The simultaneous occurrence of wind and rain can force water through building wall components such as windows, resulting in building damage and insured loss. To quantify this hazard, extreme value distributions are fit to peak 3-second wind speed data recorded during 1-minute intervals with specific reported rain intensities. This improves upon previous attempts to quantify the wind-driven rain hazard, that computed wind speed and rainfall intensity probabilities independently and used hourly data which cannot assure the simultaneous occurrence of peak wind which represents only a several-second interval within the hour and rain which is accumulated over the entire hour. The method is applied across the southeastern U.S., where the wind-driven rain hazard is most pronounced. For the lowest rainfall intensities, the computed wind speed extremes agree with published values that ignore rainfall occurrence. Such correspondence is desirable for aligning the rain-intensity-dependent windspeed return periods with established extreme wind statistics. Maximum 50-year return period wind speeds in conjunction with rainfall intensities ≥ 0.254 mm min−1 exceed 45 ms−1 in a swath from Oklahoma to the Gulf Coast and at stations along the immediate Atlantic Coast. For rainfall intensities >2.54 mm min−1 maximum, 50-year return period wind speeds decrease to 35 ms−1 but occur over a similar area. The methodology is also applied to stations outside the Southeast to demonstrate its applicability for incorporating the wind-driven rain hazard in U.S. building standards.
提出了一种求得与降雨同时发生的极端风速气候学的方法。同时发生的风和雨可以迫使水通过建筑物的墙壁组件，如窗户，造成建筑物损坏和保险损失。为了量化这种危险，极值分布适合于在特定报告降雨强度的1分钟间隔内记录的3秒风速峰值数据。这改进了以前量化风驱动雨危害的尝试，即独立计算风速和降雨强度概率，并使用每小时的数据，这些数据不能保证同时出现的峰值风(仅代表一小时内几秒钟的间隔)和整个小时累积的降雨。该方法应用于美国东南部，那里是风力驱动的降雨危险最明显的地方。对于最低降雨强度，计算的风速极值与忽略降雨发生的公布值一致。这种对应关系对于将降雨强度相关的风速返回期与已建立的极端风统计数据相一致是可取的。从俄克拉何马州到墨西哥湾沿岸和大西洋沿岸的站点，在降雨量≥0.254 mm min - 1的情况下，最大50年重现期风速超过45 ms - 1。对于最大雨强bb0 2.54 mm min - 1, 50年回复期风速降至35 ms - 1，但发生在相似的区域。该方法也被应用于东南以外的气象站，以证明其在将风雨危害纳入美国建筑标准方面的适用性。
{"title":"Development of an extreme wind-driven rain climatology for the southeastern United States using one-minute rainfall and peak wind speed data","authors":"Brian N. Belcher, A. Degaetano, F. Masters, J. Crandell, Murray J. Morrison","doi":"10.1175/jamc-d-22-0156.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0156.1","url":null,"abstract":"\u0000A method is presented to obtain the climatology of extreme wind speeds coincident with the occurrence of rain. The simultaneous occurrence of wind and rain can force water through building wall components such as windows, resulting in building damage and insured loss. To quantify this hazard, extreme value distributions are fit to peak 3-second wind speed data recorded during 1-minute intervals with specific reported rain intensities. This improves upon previous attempts to quantify the wind-driven rain hazard, that computed wind speed and rainfall intensity probabilities independently and used hourly data which cannot assure the simultaneous occurrence of peak wind which represents only a several-second interval within the hour and rain which is accumulated over the entire hour.\u0000The method is applied across the southeastern U.S., where the wind-driven rain hazard is most pronounced. For the lowest rainfall intensities, the computed wind speed extremes agree with published values that ignore rainfall occurrence. Such correspondence is desirable for aligning the rain-intensity-dependent windspeed return periods with established extreme wind statistics. Maximum 50-year return period wind speeds in conjunction with rainfall intensities ≥ 0.254 mm min−1 exceed 45 ms−1 in a swath from Oklahoma to the Gulf Coast and at stations along the immediate Atlantic Coast. For rainfall intensities >2.54 mm min−1 maximum, 50-year return period wind speeds decrease to 35 ms−1 but occur over a similar area. The methodology is also applied to stations outside the Southeast to demonstrate its applicability for incorporating the wind-driven rain hazard in U.S. building standards.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45486753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-07DOI: 10.1175/jamc-d-22-0163.1
R. Wakefield, D. Turner, T. Rosenberger, T. Heus, T. Wagner, J. Santanello, J. Basara
Land-atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site provide all of the needed inputs needed for the mixing diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer (called the Atmospheric Emitted Radiance Interferometer, or AERI) are a critical component in this analysis. Large eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. Finally, the high temporal resolution of the AERI observations enable the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models.
{"title":"A Methodology for Estimating the Energy and Moisture Budget of the Convective Boundary Layer Using Continuous Ground-based Infrared Spectrometer Observations","authors":"R. Wakefield, D. Turner, T. Rosenberger, T. Heus, T. Wagner, J. Santanello, J. Basara","doi":"10.1175/jamc-d-22-0163.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0163.1","url":null,"abstract":"\u0000Land-atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site provide all of the needed inputs needed for the mixing diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer (called the Atmospheric Emitted Radiance Interferometer, or AERI) are a critical component in this analysis. Large eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. Finally, the high temporal resolution of the AERI observations enable the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45499008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.1175/jamc-d-22-0149.1
A. Shreevastava, Colin Raymond, G. Hulley
Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift towards more humid events. Understanding the complex interactions of each heatwave type with the urban heat island are crucial for impacts, but remain understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intra-urban heat of greater Los Angeles (LA) area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research Forecast modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intra-urban variability. We find that despite temperatures being 10±3°C hotter in the September heatwave, the wet bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on non-heatwave local climates. But both types of heatwaves diminish the intra-urban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts with up to 6±0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.
{"title":"Contrasting intra-urban signatures of humid and dry heatwaves over Southern California","authors":"A. Shreevastava, Colin Raymond, G. Hulley","doi":"10.1175/jamc-d-22-0149.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0149.1","url":null,"abstract":"\u0000Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift towards more humid events. Understanding the complex interactions of each heatwave type with the urban heat island are crucial for impacts, but remain understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intra-urban heat of greater Los Angeles (LA) area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research Forecast modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intra-urban variability. We find that despite temperatures being 10±3°C hotter in the September heatwave, the wet bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on non-heatwave local climates. But both types of heatwaves diminish the intra-urban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts with up to 6±0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43728268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1175/jamc-d-22-0091.1
C. Crossett, L. Dupigny-Giroux, K. Kunkel, A. Betts, A. Bomblies
Much of the previous research on total and heavy precipitation trends across the Northeastern US (hereafter Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multi-duration (1, 2, 3, 7, 14, and 30 days), multi-return interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more frequent, shorter duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from Rotated Principal Component Analysis and k-means clustering analysis, which allowed for the main synoptic types present, as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return-intervals and included: coastal low (Nor’easters, tropical cyclones, and predecessor rain events), deep trough, east coast trough, zonal, and high pressure patterns.
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Pub Date : 2023-05-19DOI: 10.1175/jamc-d-22-0110.1
Y. Zhang, Xiao-Gang Zheng, Xiufen Li, Jiaxin Lyu, Lanlin Zhao
The new-generation multi-satellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products (GPM-IMERG) in different climatic and topographical regions of China for the 2014-2020 period. This study showed that (1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient (R) >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April-September) (RBias=7.41%) than during the dry season (RBias=13.65%). (2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. (3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.
新一代多卫星降水算法GPM-IMERG (Integrated multi-satellite Retrievals for Global precipitation Measurement)第6版提供了高分辨率和大空间范围，可用于弥补地面观测的不足。本研究旨在评价2014-2020年GPM-IMERG V06终程产品(GPM-IMERG)在中国不同气候和地形区域的降水探测能力。研究表明:(1)GPM-IMERG能较好地捕捉中国降水的时空分布特征。在年尺度上，GPM-IMERG表现良好，相关系数(R)为0.95，相对偏倚比(RBias)为15.38% ~ 23.46%。在季节尺度上，GPM-IMERG在夏季表现最好。在月尺度上，雨季(4 - 9月)GPM-IMERG表现较好(RBias=7.41%)，旱季的RBias=13.65%;(2) GPM-IMERG在西南、华中、华东、华南、东北、华北地区表现较好，在西北、西藏地区表现较差;(3)气候区对中国GPM-IMERG精度的影响最大，其次是海拔，干旱半干旱区GPM-IMERG偏差的主要来源是错过降水和假降水。而随海拔升高，误降水和假降水的影响逐渐增大。尽管GPM-IMERG与地面降水估计值之间存在明显差异，但研究结果强调了GPM-IMERG作为监测世界许多地区缺乏的高分辨率降水信息的宝贵资源的潜力。
{"title":"Evaluation of the GPM-IMERG V06 Final Run products for monthly/annual precipitation under the complex climatic and topographic conditions of China","authors":"Y. Zhang, Xiao-Gang Zheng, Xiufen Li, Jiaxin Lyu, Lanlin Zhao","doi":"10.1175/jamc-d-22-0110.1","DOIUrl":"https://doi.org/10.1175/jamc-d-22-0110.1","url":null,"abstract":"\u0000The new-generation multi-satellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products (GPM-IMERG) in different climatic and topographical regions of China for the 2014-2020 period. This study showed that (1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient (R) >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April-September) (RBias=7.41%) than during the dry season (RBias=13.65%). (2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. (3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44131493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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