Horizontal Water Vapor Transport Research Articles

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

Abstract Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model”, of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific Jet. These findings of the source of skillful subseasonal forecasts over the US West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the US West Coast seeking to make informed decisions based on extended weather prediction.

Occurrence Frequency of Global Atmospheric River (AR) Events: A Data Fusion Analysis of 12 Identification Data Sets

AbstractThe atmospheric river (AR) is a long, narrow, and transient corridor of strong horizontal water vapor transport. Various AR detection methods have been proposed, which have introduced significant uncertainty to the identified AR characteristics. This study has designed a data fusion algorithm to merge 12 data sets of different global and regional AR identification algorithms published by the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) covering the period from 1980 to 2016. It aims to conduct frequency statistics to further research the global distribution, interannual variation, the trends in poleward shifting, and the impacted factors of AR occurrence. The quantitative results indicate an overall increasing trend in interannual variation, with a more pronounced growth trend observed in the oceanic region between 40 and 60ºS. Additionally, the study identifies a poleward shift in the peak latitude of AR occurrence frequency, with speeds of 0.589° and 0.769° per decade in the Northern and Southern Hemispheres, respectively. This shift may be associated with the tropical poleward expansion. Upon examining the relationship between AR frequency and sea surface temperature (SST) as well as zonal wind, the study finds that distinct dominant factors influence AR in different regions. AR events near the 30°N/S ocean are influenced more significantly by zonal wind than by SST. These findings shed light on the global characteristics of AR occurrences and provide insights into the factors governing their variability across different areas.

Quantifying the impact of assimilating sensitive observations on heavy precipitation forecast with ensemble-based forecast sensitivity to observations technique

In this study, the Ensemble-based Forecast Sensitivity to Observations (EFSO) technique was used to examine the impact of assimilated sensitive observations on the forecast of heavy rainfall through three data assimilation strategies, i.e., running without observation assimilation (CNTL); with assimilation of all observations (CNTL_DA); with assimilation of sensitive observations (SEN_DA). The Infrared Atmospheric Sounder Interferometer presented the largest positive impact on the forecast, followed by the Advanced Microwave Sounding Unit A and surface land reports. The top 10% of observations contributing the largest positive impact on the forecast were located in the upstream ocean of the low-level winds associated with precipitation maximum. Compared to ensemble CNTL and CNTL_DA experiments, ensemble SEN_DA exhibited better precipitation probability, in which the representative member in ensemble SEN_DA successfully predicted the precipitation magnitude and location. Based upon the representative experiments of the ensemble CNTL, CNTL_DA, and SEN_DA, synoptic conditions were examined to understand how the SEN_DA improves the forecast of heavy precipitation. The three representative experiments exhibited similar characteristics and variations in large-scale circulation, horizontal water vapor transport, and wind fields. However, compared to the representative experiments of CNTL and CNTL_DA, the advection and barotropic components of potential divergence contributed to the increase and persistence of convective instability throughout heavy rainfall in SEN_DA. Heavy precipitation was closely associated with the intensity, duration, and location of the maximum convective instability. Furthermore, the release of convective instability depended on the mesoscale ascent, which determined the maximum and location of the precipitation. These results help to understand the optimization of observational design and assimilation strategy.

Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach

Atmospheric Rivers (ARs) are narrow bands of high-water vapor content in the low troposphere of mid-latitude regions through which most of the poleward moisture is being transported. ARs have been represented statistically as the regions of intense vertically integrated horizontal water vapor transport (IVT) in the atmosphere. These ARs have been found positively correlated with extreme precipitation and flood events at some coastal mid-latitude regions and thus have been linked to several socioeconomic implications. The robust and accurate forecasts of AR availability at a significant lead time can be a useful tool for managing AR-associated floods and water resources. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, we have explored some popular deep-learning architectures for predicting AR availability. AR availability maps derived from the statistical characterization of IVT using ERA5 reanalyses data of ECMWF from the testing dataset are taken as ground truth for the prediction. The predictions of the models have been analyzed based on popularly adopted performance evaluation metrics structural similarity index measure (SSIM), mean square error (MSE), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR). Our proposed autoencoder model outperforms the conventional convolutional neural network (CNN) and Conv-LSTM model. We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.424) as well as lower scores (average) of RMSE (0.155) and MSE (0.025) for the predictions which signify the ability of our model to learn spatiotemporal features linked with AR-dynamics.

The Spring Drought in Yunnan Province of China: Variation Characteristics, Leading Impact Factors, and Physical Mechanisms

Yunnan Province in Southwest China is vulnerable to droughts due to its distinctive topography and local climate. Spring drought in Yunnan (SDY), which accounts for 70% of all drought events, causes the most severe devastation. By examining the variation characteristics of droughts in Yunnan from 1961 to 2020 in terms of the standardized precipitation evapotranspiration index (SPEI), this present study shows that droughts in Yunnan have worsened in the past 60 years on different timescales. Especially, the SDY exhibits notable interannual and interdecadal variations, with no significant long-term trend, although the spring average regional temperatures have risen at a rate of 0.33 °C/10a since 1961. Here, in order to quantify the contribution of the precipitation and temperature, the two main meteorological impact factors, to the SDY under the exacerbation of climate warming, the statistical analyses reveal that precipitation plays a more crucial role than temperature in interannual and interdecadal SDY variations. Further, a diagnostic analysis of the moisture budget equation indicates that suppressed vertical moisture advection is the most important physical process affecting the reduced rainfall amount in spring, followed by the restricted horizontal water vapor transport. Meanwhile, the weak Bay of Bengal (BOB) summer monsoon, which is likely regulated by El Niño-like sea surface temperature anomalies (SSTAs) in spring, is closely linked with the SDY. This mechanism provides the possibility of SDY predictability on a seasonal scale.

Sensitivity of the simulation of extreme precipitation events in China to different cumulus parameterization schemes and the underlying mechanisms

The ability of climate models to capture extreme precipitation events is crucially important, but most of the existing models contain significant biases for the simulation of extreme precipitation. To understand the causes of these biases, we used five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting (CWRF) model to investigate its performance and biases in the simulation of extreme precipitation events in China. In general, the ensemble cumulus parameterization (ECP) scheme was the most skillful in reproducing the spatial distribution of the 95th percentile daily precipitation (P95) and the other four schemes either overestimated (the Kain-Fritsch Eta and Tiedtke schemes) or underestimated (the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes) P95. Compared with the observational data, ECP scheme significantly improved the simulation of extreme precipitation in China and had the highest correlation and the smallest root-mean-square error in most areas and seasons. To clarify the underlying physical processes of P95 simulation biases, we established a regression model of extreme precipitation based on ECP scheme. This showed that P95 in North China is mainly affected by moisture convergence, planetary boundary layer height and lifting condensation level (relative importance 18–32%). In Central China, the vertical upward motion of water vapor, sensible heat flux and planetary boundary layer height (relative importance 18–30%) are main factors associated with P95. In South China, the vertical upward motion and horizontal transport of water vapor are predominant (relative importance 26–37%). In addition, the net surface energy, surface and atmospheric radiation flux, total precipitable water, convective available potential energy and cloud water path also have a high correlation with P95 (the second most important factor; relative importance 14–31%). The influence of each factor on the simulation of P95 is different when using different cumulus parameterization schemes and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation. These results provide important references for future model evaluations and improvements.

Numerical study on advective fog formation and its characteristic associated with cold water upwelling.

Recent rapid industrial development in the Korean Peninsula has increased the impacts of meteorological disasters on marine and coastal environments. In particular, marine fog driven by summer cold water masses can inhibit transport and aviation; yet a lack of observational data hinders our understanding of this phenomena. The present study aimed to analyze the differences in cold water mass formation according to sea surface temperature (SST) resolution and its effects on the occurrence and distribution of sea fog over the Korean Peninsula from June 23-July 1, 2016, according to the Weather Research and Forecasting model. Data from the Final Operational Model Global Tropospheric Analyses were provided at 1° and 0.25° resolutions and NOAA real-time global SST (RTG-SST) data were provided at 0.083°. While conventional analyses have used initial SST distributions throughout the entire simulation period, small-scale, rapidly developing oceanic phenomena (e.g., cold water masses) lasting for several days act as an important mediating factor between the lower atmosphere and sea. RTG-SST was successful at identifying fog presence and maintained the most extensive horizontal distribution of cold water masses. In addition, it was confirmed that the difference in SST resolution led to varying sizes and strengths of the warm pools that provided water vapor from the open sea area to the atmosphere. On examining the horizontal water vapor transport and the vertical structure of the generated sea fog using the RTG-SST, water vapors were found to be continuously introduced by the southwesterly winds from June 29 to 30, creating a fog event throughout June 30. Accordingly, high-resolution SST data must be input into numerical models whenever possible. It is expected that the findings of this study can contribute to the reduction of ship accidents via the accurate simulation of sea fog.

Atmospheric rivers fueling the intensification of fog and haze over Indo-Gangetic Plains

Indo-Gangetic Plains (IGP) experiences persistent and widespread rise of fog and haze during the winter season. This has been attributed to the rise in pollution levels and water vapor, but the reason for enhancement in latter is not clear yet. We detect moisture incursion from Arabian Sea, a phenomenon called atmospheric rivers (AR), land-falling intermittently along 12–25° N corridor of the west-coast of India during winter; using satellite and reanalysis data. The total vertically integrated horizontal water vapor transport in AR-landfalls ranging from 0.7 × 108 to 2.2 × 108 kg/s; nearly five-orders of magnitude larger than the average discharge of liquid water from Indus River into Arabian Sea. These AR events are playing prominent role in enhancing water vapor over IGP region by 19 ± 5%; in turn fueling the intensification of fog and haze through aerosol-water vapor interaction. We found that AR events enhanced aerosol optical depths over IGP by about 29 ± 13%. The progression of moist-laden winds in ARs onto Himalayan Mountains contributes to the precipitation that explains the observed rise in the extreme flow of western Himalayan Rivers in winter. We conclude that these ARs likely contribute to the decline of snow albedo as pollution-mixed-ARs encounter Hindukush-Karakoram-Himalayan mountain region.

An MPI+OpenACC-based PRM scalar advection scheme in the GRAPES model over a cluster with multiple CPUs and GPUs

A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor. The Piecewise Rational Method (PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System (GRAPES) solves the moisture flux advection equation based on PRM. Computation of the scalar advection involves boundary exchange, and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES. Recently, Graphics Processing Units (GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator (OpenACC). Herein, we present an accelerated PRM scalar advection scheme with Message Passing Interface (MPI) and OpenACC to fully exploit GPUs' power over a cluster with multiple Central Processing Units (CPUs) and GPUs, together with optimization of various parameters such as minimizing data transfer, memory coalescing, exposing more parallelism, and overlapping computation with data transfers. Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme's elapsed time on a node with two GPUs (NVIDIA P100) and two 16-core CPUs (Intel Gold 6142). Further, results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.

Constraining and Characterizing the Size of Atmospheric Rivers: A Perspective Independent From the Detection Algorithm

AbstractAtmospheric rivers (AR) are large and narrow filaments of poleward horizontal water vapor transport. Because of its direct relationship with horizontal vapor transport, extreme precipitation, and overall AR impacts over land, the AR size is an important characteristic that needs to be better understood. Current AR detection and tracking algorithms have resulted in large uncertainty in estimating AR sizes, with areas varying over several orders of magnitude among different detection methods. We develop and implement five independent size estimation methods to characterize the size of ARs that make landfall over the west coast of North America in the 1980–2017 period and reduce the range of size estimation from ARTMIP. ARs that originate in the Northwest Pacific (WP) (100°−180°E) have larger sizes and are more zonally oriented than those from the Northeast Pacific (EP) (180°−240°E). ARs become smaller through their life cycle, mainly due to reductions in their width. They also become more meridionally oriented toward the end of their life cycle. Overall, the size estimation methods proposed in this study provide a range of AR areas (between 7 × 1011 and 1013 m2), that is, several orders of magnitude narrower than current methods estimation. This methodology can provide statistical constraints in size and geometry for the AR detection and tracking algorithms, and an objective insight for future studies about AR size changes under different climate scenarios.

Extreme Surface Winds During Landfalling Atmospheric Rivers: The Modulating Role of Near-Surface Stability

AbstractAtmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT and AIRS satellite observations are used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), and integrated water vapor transport (IVT) are analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22-38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36-52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% compared to 52% explained variance for USA West Coast). Use of an alternate static stability measure–the bulk Richardson number–yields a similar explained variance (47%). Lastly, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3%/14.7% during weak/strong IVT) than in stable conditions (0.58%/6.15%).

A multimodel evaluation of the water vapor budget in atmospheric rivers.

Atmospheric rivers (ARs) are narrow regions of strong horizontal water vapor transport that play important roles in the global water cycle, weather, and hydrology. Motivated by challenges in simulating ARs with state-of-the-art global models, this paper diagnoses model errors with a focus on relative contributions of moisture convergence, evaporation, and precipitation to AR column-integrated water vapor (IWV) budget. Using 20-year simulations by 24 global weather/climate models, budget terms are calculated for four AR sectors: postfrontal, frontal, prefrontal, and pre-AR, with biases assessed against two reanalysis products. The results indicate that each sector is unique in terms of the dominant water vapor balance, and that the terms exhibiting the largest intermodel spread are the same terms dominating the water vapor balance in each sector. Overall, simulated bulk AR characteristics (e.g., geometry, frequency, and intensity) are more sensitive to biases in IVT convergence and IWV tendency than to biases in evaporation and precipitation, although evaporation/precipitation biases do affect key AR bulk characteristics in selected sectors. The large intermodel spread (particularly for precipitation) and, in certain cases, discrepancies between the reanalysis references themselves (particularly for precipitation types) highlight the need for observational efforts that target better constraining AR processes in weather/climate models and reanalyses.

Dusty Atmospheric Rivers: Characteristics and Origins

AbstractAtmospheric rivers (AR) are narrow bands of strong horizontal transport of water vapor in the midlatitudes that can cause extreme precipitation, which contributes to beneficial water supply and sometimes flooding. The precipitation productivity of an AR is affected by microphysical processes, including the influence of aerosols. Earlier case studies have shown that some ARs over the North Pacific Ocean contain dust from Africa and Asia that can strongly influence precipitation by acting as ice nuclei. This paper explores how commonly dust and ARs occur together, or in close proximity. A “dust score” is introduced to characterize the dustiness of the environment associated with ARs by using satellite-based observations. This method is applied to days on which one or more ARs made landfall along the west coast of the United States between 2001 and 2018. The dust score is used to describe the seasonality and year-to-year variability of dusty-environment ARs. Dusty ARs occur primarily in the early spring (March–April), and dust is preferentially found within the warm sector of AR-associated extratropical cyclones and is also enhanced in the cold sector. Year-to-year variability in dust score is dependent more on year-to-year variability in dust than on the frequency of AR days. This year-to-year variability is also strongly related to correlations between the frequency of ARs and the dustiness of the northeastern Pacific, motivating additional investigation into potential dynamical association between dust and ARs.

A Study on the Trend of the Upper Tropospheric Water Vapor over the Tibetan Plateau in Summer

Using ERA-Interim and ERA-5 reanalysis datasets combined with measurements from the Microwave Limb Sounder (MLS), we divided the Tibetan Plateau (TP) into three regions to analyse the long-term trends of water vapor in the upper troposphere and its possible mechanisms. The conclusions are as follows. The water vapor at 200 hPa in the summers from 1979 to 2016 over Region 1 (34°N-40°N, 80°E-100°E) shows a positive trend, which is caused by the rising surface temperature and the northward shift of the South Asian High (SAH). The positive trend of water vapor over Region 2 (28°N-33°N, 80°E-90°E) is attributed to the enhanced South Asian summer monsoon, which leads to increases in horizontal transport of water vapor and vertical water vapor flux. The increase in the vertical transport of water vapor is responsible for the increase in water vapor over the Region 2. The negative trend of water vapor over Region 3 (27°N-30°N, 93°E-98°E) is mainly due to the weakening of water vapor transport caused by the weakening of convective activities.

Predictive skill for atmospheric rivers in the western Iberian Peninsula

Abstract. A large fraction of extreme precipitation and flood events across western Europe are triggered by atmospheric rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins. Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 d for events where ARs made landfall in the western Iberian Peninsula during the winters spanning between 2012–2013 and 2015–2016. Vertically integrated horizontal water vapor transport (IVT) and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and the contiguous North Atlantic Ocean. Metrics concerning AR location, intensity, and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation. We considered several regional boxes over western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill in detecting upcoming AR events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using receiver operating characteristic (ROC) analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (∼10 d). Furthermore, we show that this reversal occurs for shorter lead times in areas where the AR contribution is more relevant for winter precipitation totals (e.g., northwestern Iberia).

The diurnal transport of atmospheric water vapor during major dust storms on Mars based on the Mars Climate Database, version 5.3

In recent studies of the Martian atmosphere, strong diurnal variation in the dust was discovered in the southern hemisphere during major dust storms, which provides strong evidence that the commonly recognized meridional transport process is driven by thermal tides. This process, when coupled with deep convection, could be an important part of the short-term atmospheric dynamics of water escape. However, the potential of this process to alter the horizontal distribution of moist air has not been systematically investigated. In this work, we conducted pre-research on the horizontal transport of water vapor associated with the migrating diurnal tide (DW1) at 50 Pa in the upper troposphere during major dust storms based on the Mars Climate Database (MCD) 5.3, a state-of-the-art database for Martian atmospheric research that has been validated as simulating the relevant short-period atmospheric dynamics well. We found westward-propagating diurnal patterns in the global water vapor front during nearly all the major dust storms from Martian years (MYs) 24 to 32. Statistical and correlation analyses showed that the diurnal transport of water vapor during global and A-season regional dust storms is dominated by the DW1. The effect of the tidal transport of water vapor varies with the types of dust storms in different seasons. During regional dust storms, the tidal transport induces only limited diurnal motion of the water vapor. However, the horizontal tidal wind tends to increase the abundance of daytime water vapor at mid- to low latitudes during the MY 28 southern summer global dust storm while decreasing it during the MY 25 southern spring global dust storm. The tidal transport process during these two global dust storms can induce opposite effects on water escape.

North Atlantic Integrated Water Vapor Transport—From 850 to 2100 CE: Impacts on Western European Rainfall

AbstractMoisture transport over the northeastern Atlantic Ocean is an important process governing precipitation distribution and variability over western Europe. To assess its long-term variability, the vertically integrated horizontal water vapor transport (IVT) from a long-term climate simulation spanning the period 850–2100 CE was used. Results show a steady increase in moisture transport toward western Europe since the late-nineteenth century that is projected to expand during the twenty-first century under the RCP8.5 scenario. The projected IVT for 2070–99 significantly exceeds the range given by interannual–interdecadal variability of the last millennium. Changes in IVT are in line with significant increases in tropospheric moisture content, driven by the concurrent rise in surface temperatures associated with the anthropogenic climate trend. On regional scales, recent and projected precipitation changes over the British Isles follow the global positive IVT trend, whereas a robust precipitation decrease over Iberia is identified in the twenty-first century, particularly during autumn. This indicates a possible extension of stable and dry summer conditions and a decoupling between moisture availability and dynamical forcing. The investigation of circulation features reveals a mean poleward shift of moisture corridors and associated atmospheric rivers. In particular, in Iberia, a significant increase in the frequency of dry weather types is observed, accompanied by a decrease in the frequency of wet types. An opposite response is observed over the British Isles. These changes imply a stronger meridional north–south dipole in terms of pressure and precipitation distributions, enhancing the transport toward central Europe rather than to Iberia.

Dynamic Mapping of the Movement of Landfalling Atmospheric Rivers Over Southern California With GPS Data

AbstractAtmospheric rivers (ARs) are long, narrow, and transient corridors of strong horizontal water vapor transport that can result in heavy precipitation. Measuring the movement of these concentrated water vapor bands is important in gaining better insight into AR characteristics and forecasts of AR‐caused precipitation. We describe a method to dynamically map the movement of landfalling ARs. The method utilizes high‐rate GPS observations from a dense network to derive isochrones that represent the AR arrival time over specific locations. The generated isochrones show that the three ARs, during landfall over Southern California in January 2017, moved southeastward and took about 10 hr to pass over the study area. Overlaying the topography with isochrones reveals that the Peninsular Ranges slow the movement of the landfalling ARs. The large spacing between two adjacent isochrones, reflecting fast AR movement, is closely related to the increased hourly rain rate.

Influence of atmospheric rivers in the occurrence of devastating flood associated with extreme precipitation events over Chennai using different reanalysis data sets

The main focus of the present study is to examine the presence of Atmospheric Rivers (ARs) in the occurrence of Chennai (13.5° N, 80.5° E) flood event over Tamil Nadu which is south-eastern part of Indian peninsular region of India during November to December 2015. The vertically integrated horizontal water vapor transport (IVT) algorithm was used for the detection of ARs using three different reanalysis products, namely, the NCEP-NCAR (National Center for Atmospheric Research), NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ECMWF Interim Re-Analysis (ERA-Interim). It is noticed that the occurrence of heavy precipitation events (HPEs) prior to the flood in the Chennai city, has been in good agreement with the presence of ARs during that time. The distribution from TRMM-3B42RT, TRMM-3B42 are validated with the with in situ rain gauge observations over the Chennai city. We have conducted quantitative analysis of IVT and their impact on heavy precipitation events and flood occurred over Chennai city. It is seen that MERRA reanalysis could be able to provide better presence of ARs. The study mainly reveals that persistent ARs of >18 h resulted in extremely heavy precipitation and lead to associated flood over Chennai. Also, the atmospheric conditions, ARs and rainfall simulated from Advanced Research Weather Research and Forecasting model (ARW) show good agreement with reanalysis and observations. Over the given reference period 1979–2015, correlation between IVT and HPEs is statistically significant at 99.5% confidence level. But the correlation between IVT during persistent ARs and corresponding HPEs is statistically significant at 90% confidence level. A large fraction of HPEs occur after ARs, with a small portion of ARs would lead to HPEs. The Chennai gird historical rainfall data is suitably fitted by Extreme Value Distribution (EVD) models. Inference model using the Maximum Likelihood Estimation (MLE) was applied on EVD considering their impact on the shape parameter and the confidence interval width. A Non-stationary generalized extreme value (GEV) analysis, was used to determine the strength of association between the precipitation extremes and IVT. The influence of the location and the scale parameter in the GEV model on different return levels (2-year, 20-year, and 100-year) are explicitly described. The return-level threshold values are found to change with IVT considerably. This study advocates that the detection of ARs can be used as important proxy in identifying the likelihood of occurrence of heavy precipitation/flash flood events for a given location using reanalysis data sets.

Precipitation in a warming world: Assessing projected hydro-climate changes in California and other Mediterranean climate regions

In most Mediterranean climate (MedClim) regions around the world, global climate models (GCMs) consistently project drier futures. In California, however, projections of changes in annual precipitation are inconsistent. Analysis of daily precipitation in 30 GCMs reveals patterns in projected hydrometeorology over each of the five MedClm regions globally and helps disentangle their causes. MedClim regions, except California, are expected to dry via decreased frequency of winter precipitation. Frequencies of extreme precipitation, however, are projected to increase over the two MedClim regions of the Northern Hemisphere where projected warming is strongest. The increase in heavy and extreme precipitation is particularly robust over California, where it is only partially offset by projected decreases in low-medium intensity precipitation. Over the Mediterranean Basin, however, losses from decreasing frequency of low-medium-intensity precipitation are projected to dominate gains from intensifying projected extreme precipitation. MedClim regions are projected to become more sub-tropical, i.e. made dryer via pole-ward expanding subtropical subsidence. California’s more nuanced hydrological future reflects a precarious balance between the expanding subtropical high from the south and the south-eastward extending Aleutian low from the north-west. These dynamical mechanisms and thermodynamic moistening of the warming atmosphere result in increased horizontal water vapor transport, bolstering extreme precipitation events.