Abstract
Although environmental controls on bulk supercell potential and hazards have been studied extensively, relationships between environmental conditions and temporal changes to storm morphology remain less explored. These relationships are examined in this study using a compilation of sounding data collected during field campaigns from 1994 to 2019 in the vicinity of 216 supercells. Environmental parameters are calculated from the soundings and related to storm-track characteristics like initial cell motion and the time of the right turn (i.e., the time elapsed between the cell initiation and the first time when the supercell obtains a quasi-steady motion that is directed clockwise from its initial motion.). We do not find any significant associations between environmental parameters and the time of the right turn. Somewhat surprisingly, no relationship is found between storm-relative environmental helicity and the time elapsed between cell initiation and the onset of deviant motion. Initial cell motion is best approximated by the direction of the 0–6-km mean wind at two-thirds the speed. This is a result of advection and propagation in the 0–4- and 0–2-km layers, respectively. Unsurprisingly, Bunkers-right storm motion is a good estimate of post-turn motion, but storms that exhibit a post-turn motion left of Bunkers-right are less likely to be tornadic. These findings are relevant for real-time forecasting efforts in predicting the path and tornado potential of supercells up to hours in advance.
Abstract
Although environmental controls on bulk supercell potential and hazards have been studied extensively, relationships between environmental conditions and temporal changes to storm morphology remain less explored. These relationships are examined in this study using a compilation of sounding data collected during field campaigns from 1994 to 2019 in the vicinity of 216 supercells. Environmental parameters are calculated from the soundings and related to storm-track characteristics like initial cell motion and the time of the right turn (i.e., the time elapsed between the cell initiation and the first time when the supercell obtains a quasi-steady motion that is directed clockwise from its initial motion.). We do not find any significant associations between environmental parameters and the time of the right turn. Somewhat surprisingly, no relationship is found between storm-relative environmental helicity and the time elapsed between cell initiation and the onset of deviant motion. Initial cell motion is best approximated by the direction of the 0–6-km mean wind at two-thirds the speed. This is a result of advection and propagation in the 0–4- and 0–2-km layers, respectively. Unsurprisingly, Bunkers-right storm motion is a good estimate of post-turn motion, but storms that exhibit a post-turn motion left of Bunkers-right are less likely to be tornadic. These findings are relevant for real-time forecasting efforts in predicting the path and tornado potential of supercells up to hours in advance.
Abstract
Operational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future.
Abstract
Operational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future.
Abstract
Taiwan regularly experiences precipitation extremes of hundreds of millimeters per day, especially between May and September. In this study, Taiwan’s extreme rainfall (ER) is analyzed over a 56-yr time period in different seasons and geographic regions, using a recently released, high-resolution gridded rainfall dataset. ER is defined using a seasonally and geographically varying 99th-percentile threshold to better resolve the characteristics of the most intense rainfall seen in different locations and times of year. The resulting monthly ER rates are largest in typhoon season and smallest in fall, winter, and spring. ER is spatially homogeneous in the mei-yu and typhoon seasons and concentrated in northern Taiwan during the rest of the year. A trend analysis revealed a positive trend in island-mean ER for the winter, spring, and typhoon seasons. In winter and spring, these trends are most pronounced in the north. In the mei-yu season, ER has increased most over the southwestern mountain slopes; in typhoon season, ER has increased consistently over much of Taiwan. These changes often exceed 1% yr−1. In many areas, typhoon season accounts for the largest fraction of the observed annual ER trend. TCs produce most of the observed typhoon season ER and ER trend, with nearly half of the typhoon season ER trend being associated with increases in TC frequency and duration around central and northern Taiwan. Certain regional changes in ER characteristics, particularly in areas with low sample size or complex seasonal contributions, merit further investigation in future work.
Abstract
Taiwan regularly experiences precipitation extremes of hundreds of millimeters per day, especially between May and September. In this study, Taiwan’s extreme rainfall (ER) is analyzed over a 56-yr time period in different seasons and geographic regions, using a recently released, high-resolution gridded rainfall dataset. ER is defined using a seasonally and geographically varying 99th-percentile threshold to better resolve the characteristics of the most intense rainfall seen in different locations and times of year. The resulting monthly ER rates are largest in typhoon season and smallest in fall, winter, and spring. ER is spatially homogeneous in the mei-yu and typhoon seasons and concentrated in northern Taiwan during the rest of the year. A trend analysis revealed a positive trend in island-mean ER for the winter, spring, and typhoon seasons. In winter and spring, these trends are most pronounced in the north. In the mei-yu season, ER has increased most over the southwestern mountain slopes; in typhoon season, ER has increased consistently over much of Taiwan. These changes often exceed 1% yr−1. In many areas, typhoon season accounts for the largest fraction of the observed annual ER trend. TCs produce most of the observed typhoon season ER and ER trend, with nearly half of the typhoon season ER trend being associated with increases in TC frequency and duration around central and northern Taiwan. Certain regional changes in ER characteristics, particularly in areas with low sample size or complex seasonal contributions, merit further investigation in future work.
Abstract
Paleoproxy records indicate that abrupt changes in thermohaline circulation (THC) were induced by rapid meltwater discharge from retreating ice sheets. Such abrupt changes in the THC have been understood as a hysteresis behavior of a nonlinear system. Previous studies, however, primarily focused on a near-static hysteresis under fixed or slowly varying freshwater forcing (FWF), reflecting the equilibrated response of the THC. This study aims to improve the current understanding of transient THC responses under rapidly varying forcing and their dependency on forcing time scales. The results simulated by an Earth system model suggest that the bifurcation is delayed as the forcing time scale is shorter, causing the Atlantic meridional overturning circulation collapse and recovery to occur at higher and lower FWF values, respectively. The delayed shutdown/recovery occurs because bifurcation is determined not by the FWF value at the time but by the total amount of freshwater remaining over the THC convection region. The remaining freshwater amount is primarily determined by the forcing accumulation (i.e., time-integrated FWF), which is modulated by the freshwater/salt advection by ocean circulations and freshwater flux by the atmospheric hydrological cycle. In general, the latter is overwhelmed by the former. When the forced freshwater amount is the same, the modulation effect is stronger under slowly varying forcing because more time is provided for the feedback processes.
Abstract
Paleoproxy records indicate that abrupt changes in thermohaline circulation (THC) were induced by rapid meltwater discharge from retreating ice sheets. Such abrupt changes in the THC have been understood as a hysteresis behavior of a nonlinear system. Previous studies, however, primarily focused on a near-static hysteresis under fixed or slowly varying freshwater forcing (FWF), reflecting the equilibrated response of the THC. This study aims to improve the current understanding of transient THC responses under rapidly varying forcing and their dependency on forcing time scales. The results simulated by an Earth system model suggest that the bifurcation is delayed as the forcing time scale is shorter, causing the Atlantic meridional overturning circulation collapse and recovery to occur at higher and lower FWF values, respectively. The delayed shutdown/recovery occurs because bifurcation is determined not by the FWF value at the time but by the total amount of freshwater remaining over the THC convection region. The remaining freshwater amount is primarily determined by the forcing accumulation (i.e., time-integrated FWF), which is modulated by the freshwater/salt advection by ocean circulations and freshwater flux by the atmospheric hydrological cycle. In general, the latter is overwhelmed by the former. When the forced freshwater amount is the same, the modulation effect is stronger under slowly varying forcing because more time is provided for the feedback processes.
Abstract
Tornadoes cause billions of dollars in damage and over 100 fatalities on average annually. Yet, an indirect cost to these storms is found in lost sales and/or lost productivity from responding to over 2000 warnings per year. This project responds to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. Our goal is to provide an analysis of cost avoidance that could accrue from a change to the warning paradigm, particularly to include probabilistic hazard information at storm scales. A survey of nearly 500 firms was conducted in and near the Dallas–Fort Worth metropolitan area asking questions about experience with tornadoes, sources of information for severe weather, expected cost of responding to tornado warnings, and how the firm would respond to either deterministic or probabilistic warnings. We find a dramatic change from deterministic warnings compared to the proposed probabilistic and that a probabilistic information system produces annual cost avoidance in a range of $2.3–$7.6 billion (U.S. dollars) compared to the current deterministic warning paradigm.
Abstract
Tornadoes cause billions of dollars in damage and over 100 fatalities on average annually. Yet, an indirect cost to these storms is found in lost sales and/or lost productivity from responding to over 2000 warnings per year. This project responds to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. Our goal is to provide an analysis of cost avoidance that could accrue from a change to the warning paradigm, particularly to include probabilistic hazard information at storm scales. A survey of nearly 500 firms was conducted in and near the Dallas–Fort Worth metropolitan area asking questions about experience with tornadoes, sources of information for severe weather, expected cost of responding to tornado warnings, and how the firm would respond to either deterministic or probabilistic warnings. We find a dramatic change from deterministic warnings compared to the proposed probabilistic and that a probabilistic information system produces annual cost avoidance in a range of $2.3–$7.6 billion (U.S. dollars) compared to the current deterministic warning paradigm.
Abstract
The prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden–Julian oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to be more skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3–4 and phase 7 have better NAO forecast skills compared with the other phases. The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “windows of opportunity” for subseasonal to seasonal predictions.
Abstract
The prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden–Julian oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to be more skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3–4 and phase 7 have better NAO forecast skills compared with the other phases. The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “windows of opportunity” for subseasonal to seasonal predictions.
Abstract
This study utilizes brightness temperatures (T bs) observed by the infrared longwave window band (channel 14; 11.2 μm) from the Geostationary Operational Environmental Satellite-16 (GOES-16) to examine the structure of Hurricanes Harvey, Maria, and Michael throughout their lifetimes. During the times leading up to their rapid intensifications (RI), two-dimensional inner-core structures are examined to analyze the strength and location of the developing convection. Moderate vertical wind shear in the environments of Harvey and Michael induced a pronounced convective asymmetry prior to RI, followed by a rapid axisymmetrization that occurred essentially in conjunction with RI. The evolutions of the tropical cyclones’ (TCs’) coldest T bs indicate that the inner-core convective activity began to increase in the 12 h prior to RI onset, primarily in 2–4-h substantial “bursts,” while substantial convection dominated essentially the entirety of the region within 100 km of the surface center within 12 h of the onset of intensification. Azimuthally averaged T b evolutions illustrate the development of each TC’s eye and eyewall, the variability of the radial extent of the central dense overcast associated with the diurnal cycle, as well as details of the evolving convective structures throughout intensification. Hovmöller diagrams of data at constant radii reveal areas of cold T bs propagating around the TCs on time scales of 2–3 h. The examination of these features in a deep-layer shear-relative sense reveals that they initiate primarily downshear of the TCs’ surface centers. As RI is reached, these areas of convection are able to propagate into the upshear quadrants, which helps facilitate the onset of more substantial intensification.
Abstract
This study utilizes brightness temperatures (T bs) observed by the infrared longwave window band (channel 14; 11.2 μm) from the Geostationary Operational Environmental Satellite-16 (GOES-16) to examine the structure of Hurricanes Harvey, Maria, and Michael throughout their lifetimes. During the times leading up to their rapid intensifications (RI), two-dimensional inner-core structures are examined to analyze the strength and location of the developing convection. Moderate vertical wind shear in the environments of Harvey and Michael induced a pronounced convective asymmetry prior to RI, followed by a rapid axisymmetrization that occurred essentially in conjunction with RI. The evolutions of the tropical cyclones’ (TCs’) coldest T bs indicate that the inner-core convective activity began to increase in the 12 h prior to RI onset, primarily in 2–4-h substantial “bursts,” while substantial convection dominated essentially the entirety of the region within 100 km of the surface center within 12 h of the onset of intensification. Azimuthally averaged T b evolutions illustrate the development of each TC’s eye and eyewall, the variability of the radial extent of the central dense overcast associated with the diurnal cycle, as well as details of the evolving convective structures throughout intensification. Hovmöller diagrams of data at constant radii reveal areas of cold T bs propagating around the TCs on time scales of 2–3 h. The examination of these features in a deep-layer shear-relative sense reveals that they initiate primarily downshear of the TCs’ surface centers. As RI is reached, these areas of convection are able to propagate into the upshear quadrants, which helps facilitate the onset of more substantial intensification.
Abstract
The synoptic low pressure systems (LPSs) formed over the downwind side of the Tibetan Plateau explain a substantial portion of summer rainfall extremes along their paths. Recent studies have found that the total extreme rainfall trend over the East Asian landmass, which features the “south flood–north drought” pattern, can be understood to a great extent by the changes in terrestrial LPSs. Yet, the energy sources fueling these storms and the environmental drivers of their long-term trends remain unclear. Utilizing a probabilistic clustering method, three clusters of terrestrial LPS tracks for the period 1979–2018 are identified. Besides the differences in trajectories that distinguish the clusters into northeastward-migrating and quasi-stationary types, prominent intercluster differences are found in the LPS evolution, energetics, and trends. The Lorenz energetics suggest that while condensational heating is indispensable for all three clusters, the migratory type, which has greater intensity and faster development, is more closely tied to baroclinicity. Nonetheless, the summer baroclinicity alone is not enough to sustain these LPSs as these storms dissipate quickly after propagating out of the humid monsoon region and into the drier extratropics. Over time, occurrences of migratory LPSs decrease, and those of quasi-stationary LPSs increase. Using a Poisson model that links the LPS genesis to local environmental conditions, the decreasing occurrence of migratory LPSs is shown to result from the weakened baroclinicity, whereas the increasing occurrence of quasi-stationary LPSs is primarily driven by enhanced relative humidity and reduced steering flow in the mid-to-lower troposphere over East Asia.
Abstract
The synoptic low pressure systems (LPSs) formed over the downwind side of the Tibetan Plateau explain a substantial portion of summer rainfall extremes along their paths. Recent studies have found that the total extreme rainfall trend over the East Asian landmass, which features the “south flood–north drought” pattern, can be understood to a great extent by the changes in terrestrial LPSs. Yet, the energy sources fueling these storms and the environmental drivers of their long-term trends remain unclear. Utilizing a probabilistic clustering method, three clusters of terrestrial LPS tracks for the period 1979–2018 are identified. Besides the differences in trajectories that distinguish the clusters into northeastward-migrating and quasi-stationary types, prominent intercluster differences are found in the LPS evolution, energetics, and trends. The Lorenz energetics suggest that while condensational heating is indispensable for all three clusters, the migratory type, which has greater intensity and faster development, is more closely tied to baroclinicity. Nonetheless, the summer baroclinicity alone is not enough to sustain these LPSs as these storms dissipate quickly after propagating out of the humid monsoon region and into the drier extratropics. Over time, occurrences of migratory LPSs decrease, and those of quasi-stationary LPSs increase. Using a Poisson model that links the LPS genesis to local environmental conditions, the decreasing occurrence of migratory LPSs is shown to result from the weakened baroclinicity, whereas the increasing occurrence of quasi-stationary LPSs is primarily driven by enhanced relative humidity and reduced steering flow in the mid-to-lower troposphere over East Asia.
Abstract
Models disagree on how much the hydrologic cycle could intensify under climate change. These changes are expected to scale with the Clausius–Clapeyron relation but may locally diverge due in part to the uncertain response of the general circulation, causing the hydrologic cycle to inherit this uncertainty. To identify how the circulation contributes, we link circulation changes to changes in the higher moments of the hydrologic cycle using the novel dynamical framework of the local hydrologic cycle, the portion of the hydrologic cycle driven by moist or dry intrusions. We expand this dynamical framework, developing a closed budget that diagnoses thermodynamic, advective, and overturning contributions to future hydrologic cycle changes. In analyzing these changes for the Community Earth System Model Large Ensemble, we show that overturning is the main dynamic contributor to the tropical and subtropical annual response, consistent with a weakening of this circulation. In the extratropics, we show that advective contributions, likely from storm track changes, dominate the response. We achieve a cleaner separation between dynamic and thermodynamic contributions through a semiempirical scaling, which reveals the robustness of the Clausius–Clapeyron scaling for the local hydrologic cycle. This scaling also demonstrates the slowing of the local hydrologic cycle and how changing subtropical dynamics asymmetrically impact wave breaking and suppress meridional moisture transport. We conclude that dynamic changes in the subtropics are predominantly responsible for the annual, dynamic response in the extratropics and thus a significant contributor to uncertainty in future projections.
Abstract
Models disagree on how much the hydrologic cycle could intensify under climate change. These changes are expected to scale with the Clausius–Clapeyron relation but may locally diverge due in part to the uncertain response of the general circulation, causing the hydrologic cycle to inherit this uncertainty. To identify how the circulation contributes, we link circulation changes to changes in the higher moments of the hydrologic cycle using the novel dynamical framework of the local hydrologic cycle, the portion of the hydrologic cycle driven by moist or dry intrusions. We expand this dynamical framework, developing a closed budget that diagnoses thermodynamic, advective, and overturning contributions to future hydrologic cycle changes. In analyzing these changes for the Community Earth System Model Large Ensemble, we show that overturning is the main dynamic contributor to the tropical and subtropical annual response, consistent with a weakening of this circulation. In the extratropics, we show that advective contributions, likely from storm track changes, dominate the response. We achieve a cleaner separation between dynamic and thermodynamic contributions through a semiempirical scaling, which reveals the robustness of the Clausius–Clapeyron scaling for the local hydrologic cycle. This scaling also demonstrates the slowing of the local hydrologic cycle and how changing subtropical dynamics asymmetrically impact wave breaking and suppress meridional moisture transport. We conclude that dynamic changes in the subtropics are predominantly responsible for the annual, dynamic response in the extratropics and thus a significant contributor to uncertainty in future projections.
Abstract
A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.
Abstract
A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.
