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In this paper, the reliability of the wind speed, temperature, humidity, pressure, and precipitation values of three surface meteorological forcing products [China Meteorological Administration Land Data Assimilation System, version 2 (CLDAS-2); China Meteorological Forcing Dataset (CMFD); and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)] in the Tibetan Plateau (TP) region was investigated from 2008 to 2014. Compared with the China Meteorological Administration (CMA) observations, CLDAS-2 exhibited the highest correlation coefficient for wind speed, CMFD displayed the best coefficients for temperature and specific humidity, and MERRA-2 best reflected pressure variations. Based on the biases, CLDAS-2 exhibited the best overall performance for temperature, specific humidity, and pressure, while CMFD displayed the best performance for wind speed. The high overall accuracy and false alarm ratio of precipitation based on MERRA-2 both stem from its continuous overestimation of the precipitation frequency. Both CLDAS-2 and CMFD overestimated the nonprecipitation frequency in comparisons with CMA observations, and a significant positive bias exists in MERRA-2 based on the analysis of daily precipitation. The results obtained from the comparisons with field observations over the TP and CMA observations are similar, except for the temperature and humidity biases of CLDAS-2. The meteorological effects on the coupled land-blowing snow modeling discussed in this paper suggest that the occurrence of blowing snow and snowdrift sublimation are projected to be reduced by CLDAS-2 due to the underestimation of wind speed, continual lack of snowfall events, and the positive biases in low temperatures and humidity, while simulations of blowing processes by MERRA-2 are likely to be much more severe than they actually are. These results may contribute to identifying deficiencies associated with the development of land surface models coupled with a blowing snow model. [ABSTRACT FROM AUTHOR]

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG's proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG's overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region. [ABSTRACT FROM AUTHOR]

Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground observations. Here, the triple collocation (TC) method is employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependencies among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized three-cornered hat) and explore the applicability of cross validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now. Significance Statement: Cross validation is a powerful assessment method for multisource precipitation datasets. This method is widely used by hydrologists in sparse-gauge or ungauged regions, like the African continent and the Tibetan Plateau (TP). However, as an indirect assessment method, the inherent uncertainty in cross validation warrants emphasis. Here, two cross-validation methods (the triple collocation method and the generalized three-cornered hat method) are employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the TP. In this study, we not only assessed the current mainstream six precipitation datasets but also analyzed their uncertainties by combining these two cross-validation methods. [ABSTRACT FROM AUTHOR]

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces. [ABSTRACT FROM AUTHOR]

Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020. Significance Statement: Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes. [ABSTRACT FROM AUTHOR]

The CTP-HI[sub low] framework for describing atmospheric controls on soil moisture–boundary layer interactions is described in a companion paper,. In this paper, the framework is applied to the continental United States to investigate how differing atmospheric regimes influence local feedbacks between the land surface and the atmosphere. The framework was developed with a one-dimensional boundary layer model and is based on two measures of atmospheric thermodynamic properties: the convective triggering potential (CTP), a measure of the temperature lapse rate between approximately 1 and 3 km above the ground surface, and a low-level humidity index, HI[sub low] . These two measures are used to distinguish between three types of early-morning atmospheric conditions: those favoring moist convection over dry soils, those favoring moist convection over wet soils, and those that will allow or prevent deep convective activity, independent of the surface flux partitioning. Analyses of multiyear CTP-HI[sub low] scatterplots from radiosonde stations across the contiguous 48 United States reveal that during the summer months (June, July, and August) positive feedbacks between soil moisture and moist convection are likely in much of the eastern half of the country. Over the western half of the country, atmospheric conditions and the likelihood of moist convection are largely determined by oceanic influences, and land surface conditions in the summer are unlikely to impact convective triggering. The only area showing a potential negative feedback is in the dryline and monsoon region of the arid Southwest. This potential arises because of the topography of this and surrounding regions. A relatively narrow band of stations lies in between the eastern and western portions of the country, in some years behaving like the stations to the west and in other years behaving like the stations to the east. [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land-atmosphere coupling--surface flux equilibrium (SFE) theory--to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. [ABSTRACT FROM AUTHOR]

Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1-30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7-0.8 after 3-5 days, then gradually decreases and settles at approximately 0.2-0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model's prediction is 0.2-0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. [ABSTRACT FROM AUTHOR]

World extremes in meteorology are important as they can be used as indicators for climate change. This was one of the main reasons for the creation of the World Meteorological Organization's World Weather and Climate Extremes Archive in 2006. In contrast to temperature, for instance, which can be described by a single parameter, point rainfall must be described by two parameters, for example, precipitation depth and duration. This makes it difficult to directly compare different rainfall records. In this article, however, it is shown that the world's greatest rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same precipitation intensity duration index, a new dimensionless number. As a theoretical consequence, the intensity of all these record rainfalls is inversely proportional to the square root of their duration. This physically based result is consistent with earlier statistically based findings. The last measured record rainfall on the World Meteorological Organization's record list is the point rainfall with the largest precipitation intensity duration index since 1860. This 4-day rainfall that began on 24 February 2007 on Cratere Commerson, Réunion Island, can be considered the largest point rainfall within documented records. [ABSTRACT FROM AUTHOR]

This paper focuses on diagnosing the strength of soil moisture-atmosphere coupling at subseasonal to seasonal time scales over Asia using two different approaches: the conditional correlation approach [applied to the Global Land Data Assimilation System (GLDAS) data, the Climate Forecast System Reanalysis (CFSR) data, and output from the regional climate model, version 4 (RegCM4)] and the Global Land-Atmosphere Coupling Experiment (GLACE) approach applied to the RegCM4. The conditional correlation indicators derived from the model output and the two observational/reanalysis datasets agree fairly well with each other in the spatial pattern of the land-atmosphere coupling signal, although the signal in CFSR data is stronger and spatially more extensive than the GLDAS data and the RegCM4 output. Based on the impact of soil moisture on 2-m air temperature, the land-atmosphere coupling hotspots common to all three data sources include the Indochina region in spring and summer, the India region in summer and fall, and north-northeastern China and southwestern Siberia in summer. For precipitation, all data sources produce a weak and spatially scattered signal, indicating the lack of any strong coupling between soil moisture and precipitation, for both precipitation amount and frequency. Both the GLACE approach and the conditional correlation approach (applied to all three data sources) identify evaporation and evaporative fraction as important links for the coupling between soil moisture and precipitation/temperature. Results on soil moisture-temperature coupling strength from the GLACE-type experiment using RegCM4 are in good agreement with those from the conditional correlation analysis applied to output from the same model, despite substantial differences between the two approaches in the terrestrial segment of the land-atmosphere coupling. [ABSTRACT FROM AUTHOR]

Combination methods estimate the partition of sensible and latent heat fluxes at the surface by combining the surface energy balance equation with the transfer equations for temperature and water vapor in the atmospheric surface layer. This paper presents a diagram that graphically depicts the main assumptions and steps of the method. Potential, apparent potential, and actual evaporation rates are visually explained with the diagram. Using a linearized saturation water content relation, the diagram permits the derivation of many of the combination evaporation results by means of geometrical analysis. The diagram should be helpful in the teaching and understanding of the combination methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for investigating coupled land–atmosphere interactions. The method is to apply an empirical correction technique to distinct components of a model and then examine differences between forecasts of the empirically corrected models. The correction technique is based on adding a time-dependent term to the tendency equations that subtracts the estimated tendency error at every time step. This methodology can be interpreted more generally as a series of data assimilation experiments in which only certain components of a coupled model are assimilated at a time. The correction is applied to a state-of-the-art coupled land–atmosphere model in three different ways, namely, to the atmosphere only, to the land only, and to the land and atmosphere simultaneously. The land–atmosphere interactions are inferred from monthly-mean differences between experiments. The results suggest that the land–atmosphere coupling in midlatitudes can be understood from straightforward water balance considerations, whereas the coupling in the deep tropics involves a more complicated change in regional circulation. Specifically, in midlatitudes, moisture injected into the soil is transferred to the atmosphere directly above, which in turn advects downstream and subsequently moistens the atmosphere in the downwind regions to produce positive precipitation anomalies. In the deep tropics, the regional circulation, including precipitation, is sensitive to perturbations and has no obvious relation to corrections in the atmosphere or land. The similarity of biases among different models suggests that the conclusions and methodology may be relevant to other models. [ABSTRACT FROM AUTHOR]

The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role. [ABSTRACT FROM AUTHOR]

Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations. [ABSTRACT FROM AUTHOR]

This paper is Part I of a two-part study that uses high-resolution Regional Atmospheric Modeling System (RAMS) simulations to investigate mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. The focus is on the atmospheric dynamics associated with mesoscale heterogeneity in the underlying surface fluxes: how shifts in meteorological regimes modulate these diurnal, mesoscale processes, and their overall impact at larger scales and over multiple diurnal cycles. Part I examines individual case study time periods drawn from the simulations that illustrate general points about the key land–atmosphere interactions. The main findings are as follows: The mesoscale processes are embedded within a synoptic-scale organization that controls the background meteorological regime at a given location. During the clear, dry days in the simulated months, heterogeneity in the surface fluxes forces strong, lower-tropospheric, mesoscale circulations that exhibit a characteristic dynamical life cycle over diurnal time scales. In general, the background large-scale flow does not affect the overall intensity of these coherent roll structures, though strong large-scale subsidence can sometimes dampen them. In addition, depending on the thermodynamic profile, the strong vertical motions associated with these circulations are sufficient to trigger shallow or even deep convection, with associated clouds and precipitation. Furthermore, surface heterogeneity sufficient to force such circulations can arise even without heterogeneity in preexisting land cover characteristics such as vegetation, for example, solely as a result of spatial variability in rainfall and other atmospheric processes. In Part II the mesoscale land–atmosphere interactions in these case study periods are placed in the larger context of the full, monthlong simulations. [ABSTRACT FROM AUTHOR]

This is Part II of a two-part study of mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. Part I focused on case studies drawn from monthlong (July 1995–97), high-resolution Regional Atmospheric Modeling System (RAMS) simulations carried out to investigate these interactions. These case studies were chosen to highlight key features of the lower-tropospheric mesoscale circulations that frequently arise in this region and season due to mesoscale heterogeneity in the surface fluxes. In this paper, Part II, the RAMS-simulated mesoscale dynamical processes described in the Part I case studies are examined from a domain-averaged perspective to assess their importance in the overall regional hydrometeorology. The spatial statistics of key simulated mesoscale variables—for example, vertical velocity and the vertical flux of water vapor—are quantified here. Composite averages of the mesoscale and large-scale-mean variables over different meteorological or dynamical regimes are also calculated. The main finding is that, during dry periods, or similarly, during periods characterized by large-scale-mean subsidence, the characteristic signature of surface-heterogeneity-forced mesoscale circulations, including enhanced vertical motion variability and enhanced mesoscale fluxes in the lowest few kilometers of the atmosphere, consistently emerges. Furthermore, the impact of these mesoscale circulations is nonnegligible compared to the large-scale dynamics at domain-averaged (200 km × 200 km) spatial scales and weekly to monthly time scales. These findings support the hypothesis that the land– atmosphere interactions associated with mesoscale surface heterogeneity can provide pathways whereby diurnal, mesoscale atmospheric processes can scale up to have more general impacts at larger spatial scales and over longer time scales. [ABSTRACT FROM AUTHOR]

The governing equations of soil moisture dynamics, photosynthesis, and transpiration are reviewed and coupled to study the dependence of plant carbon assimilation on soil moisture. The model follows the scheme of the soil–plant–atmosphere continuum (SPAC) and uses a simplified model of the atmospheric boundary layer to arrive at an upscaled, parsimonious representation at the daily time scale. The analysis of soil moisture, transpiration, and carbon assimilation dynamics provides an assessment of the role of soil, plant, and boundary layer characteristics on the diurnal courses of photosynthesis and transpiration rates, while the subsequent upscaling at the daily level provides a functional dependence of stomatal conductance on soil moisture that is in good agreement with field experiments. The upscaled dependence of transpiration and carbon assimilation on soil moisture is used inof this paper to explore the impact of soil moisture dynamics on plant conditions when rainfall variability is explicitly considered. [ABSTRACT FROM AUTHOR]

Surface water and energy balance plays an important role in land surface models, especially in coupled land surface–atmospheric models due to the complicated interactions between land surfaces and the overlying atmosphere. The primary purpose of this paper is to demonstrate the significant negative impacts that a minor deficiency in the parameterization of canopy evaporation may have on offline and coupled land surface model simulations. In this research, using the offline NCAR Land Surface Model (LSM) and the locally coupled NCAR Single-column Community Climate Model (SCCM) as examples, intensive effort has been focused on the exploration of the mechanisms involved in the activation of unrealistically high canopy evaporation and thus unreasonable surface energy partitions because of a minor deficiency in the parameterization of canopy evaporation. The main causes responsible for exacerbating the impacts of the deficiency of the land surface model through the coupling of the two components are analyzed, along with possible impacts of land surface parameters in triggering the problems. Results from experimental runs show that, for a large number of randomly generated physically realistic land surface parameter sets, this model deficiency has caused the occurrences of negative canopy water with a significantly high frequency for both the offline NCAR LSM and the coupled NCAR SCCM, suggesting that land surface parameters are not the only important factors in triggering the problems associated with the model deficiency. In addition, the concurrence of intense solar radiation and enough precipitation is identified to be mainly responsible for exacerbating the negative impacts of the parameterization deficiency. Finally, a simple adjustment has been made in this study to effectively prevent the occurrences of negative canopy water storages, leading to significantly improved model performances. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of soil moisture on the development and triggering of convection in different early-morning atmospheric conditions. A one-dimensional model of the atmospheric boundary layer (BL) is initialized with atmospheric sounding data from Illinois and with the soil moisture set to either extremely wet (saturated) or extremely dry (20% of saturation) conditions. Two measures are developed to assess the low-level temperature and humidity structure of the early-morning atmosphere. These two measures are used to distinguish between four types of soundings, based on the likely outcome of the model: 1) those soundings favoring deep convection over dry soils, 2) those favoring deep convection over wet soils, 3) those unlikely to convect over any land surface, and 4) those likely to convect over any land surface. Examples of the first two cases are presented in detail. The early-morning atmosphere is characterized in this work by the newly developed convective triggering potential (CTP) and a low-level humidity index, HI[sub low] . The CTP measures the departure from a moist adiabatic temperature lapse rate in the region between 100 and 300 mb (about 1–3 km) above the ground surface (AGS). This region is the critical interface between the near-surface region, which is almost always incorporated into the growing BL, and free atmospheric air, which is almost never incorporated into the BL. Together, these two measures form the CTP-HI[sub low] framework for analyzing atmospheric controls on soil moisture–boundary layer interactions. Results show that in Illinois deep convection is trigged in the model 22% of the time over wet soils and only 13% of the time over dry soils. Additional testing varying the radiative conditions in Illinois and also using the 1D model with soundings from four additional stations confirm that the CTP-HI[sub low] framework is valid for regions far removed from Illinois. [ABSTRACT FROM AUTHOR]

Soil moisture--precipitation (SM-PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s-1), high convective available potential energy (>1500 J kg-1), and low convective inhibition (<250 J kg-1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM--PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM-PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions. [ABSTRACT FROM AUTHOR]

Three satellite precipitation datasets--CMORPH, PERSIANN-CDR, and GPCP--from the NOAA/ Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007-18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products' (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling--Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance--smaller bias, higher correlation, and a better KGE score--than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH's performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 08C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products. [ABSTRACT FROM AUTHOR]

In the present study, the convective event over the Black Sea area in September 2018 is analyzed using the Weather Research and Forecasting (WRF) Model configured with a fully convective-resolving setup. We test the WRF sensitivity to the choice of sea surface temperature (SST) dataset and microphysics scheme. The simulation is verified using weather radar measurements and ground observations. Both the choice of the microphysical scheme and SST dataset have a significant impact on the dynamic properties of the maritime convective system and associated rainfall. The best results are achieved with the WDM6 microphysical scheme and a more detailed and slightly warmer (compared to the default OSTIA SST) G1SST dataset. The optimally configured WRF simulations add value to coarser driving operational analysis, with more accurate amount and pattern of rainfall and the earlier arrival of the convective system, which is in better agreement with radar and weather station measurements. The vertical structure of the reflectivity profiles in the WDM6 scheme that simulates 15%–20% larger rainwater loading compared to other schemes agrees best with the observed data. Other schemes reproduce excessive reflectivity above the freezing level. Enhanced rainfall estimates and faster convective system propagation in the G1SST WDM6 simulations are linked to stronger cold pools caused by enhanced evaporation due to the higher rainwater content and droplet number concentrations. Stronger cold pools result in the 15%–20% enhancement of latent and sensible heat fluxes, reflecting the strong sensitivity of ocean–atmosphere heat and moisture exchange to the choice of microphysics scheme and SST dataset. [ABSTRACT FROM AUTHOR]

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands. Significance Statement: The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded. [ABSTRACT FROM AUTHOR]

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are examined over three coastal regions of the United States: the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes. [ABSTRACT FROM AUTHOR]

Knowledge about precipitation generation remains limited in the tropical Andes due to the lack of water stable isotope (WSI) data. Therefore, we investigated the key factors controlling the isotopic composition of precipitation in the P'aramo highlands of southern Ecuador using event-based (high frequency) WSI data collected between November 2017 and October 2018. Our results show that air masses reach the study site preferentially from the eastern flank of the Andes through the Amazon basin (73.2%), the Orinoco plains (11.2%), and the Mato Grosso Massif (2.7%), whereas only a small proportion stems from the Pacific Ocean (12.9%). A combination of local and regional factors influences the δ118O isotopic composition of precipitation. Regional atmospheric features (Atlantic moisture, evapotranspiration over the Amazon rainforest, continental rain-out, and altitudinal lapse rates) are what largely control the meteoric δ118O composition. Local precipitation, temperature, and the fraction of precipitation corresponding to moderate to heavy rainfalls are also key features influencing isotopic ratios, highlighting the importance of localized convective precipitation at the study site. Contrary to δ118O, d-excess values showed little temporal variation and could not be statistically linked to regional or local hydrometeorological features. The latter reveals that large amounts of recycled moisture from the Amazon basin contribute to local precipitation regardless of season and predominant trajectories from the east. Our findings will help to improve isotope-based climatic models and enhance paleoclimate reconstructions in the southern Ecuador highlands. [ABSTRACT FROM AUTHOR]

We perform a worldwide analysis of the raindrop size distribution using 166 disdrometer datasets from 76 distinct sites for a total of 1 527 963 one-minute drop counts, 428 410 two-minute drop counts, and a total of 988 922 720 drops. Following data science tenets, we adopt a functional-agnostic description of the raindrop size distribution. In this way, we uncover the presence of an invariant structure of statistical relationship among the distribution parameters, not depending on location, synoptic origin, or type of disdrometer. The features of this structure are 1) count--shape independence (there is no dependence between the drop count N and the shape of raindrop spectra), 2) mean--skewness prominence (the variability of the shape of raindrop spectra can be fully captured by its mean mand skewness g), and 3) mean--skewness invariant parameterization [we derive empirical invariant functional forms expressing all other shape describing parameters in terms of the free parameters (m, g)]. The presented analysis reveals the global and local properties of the raindrop size distribution offering a coherent and universally applicable methodology to describe the raindrop size distribution. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) rainfall hazard assessment is subject to the bias in TC climatology estimation from climate simulations or synthetic downscaling. In this study, we investigate the uncertainty in TC rainfall hazard assessment induced by this bias using both rain gauge and radar observations and synthetic-storm-model-coupled TC rainfall simulations. We identify the storm’s maximum intensity, impact duration, and minimal distance to the site to be the three most important storm parameters for TC rainfall hazard, and the relationship between the important storm parameters and TC rainfall can be well captured by a physics-based TC rainfall model. The uncertainty in the synthetic rainfall hazard induced by the bias in TC climatology can be largely explained by the bias in the important storm parameters simulated by the synthetic storm model. Correcting the distribution of the most biased parameter may significantly improve rainfall hazard estimation. Bias correction based on the joint distribution of the important parameters may render more accurate rainfall hazard estimations; however, the general technical difficulties in resampling from high-dimensional joint probability distributions prevent more accurate estimations in some cases. The results of the study also support future investigation of the impact of climate change on TC rainfall hazards through the lens of future changes in the identified important storm parameters. [ABSTRACT FROM AUTHOR]

In this study, we used hourly observations to investigate the cooling effect of summer rainfall on surface air temperature (Ta) in a subtropical area, Guangdong province, South China. Data were categorized step-by-step by rainfall system (convection, monsoon, and typhoon), daily rainfall amount, and relative humidity (RH) level. Moreover, the average hourly Ta variation due to solar radiation was removed from all observations before statistical analysis. The results showed that the linear relationship between hourly Ta variation and rainfall intensity did not exist. However, the cooling effect of rainfall on Ta variation was dominant. In addition, convective rainfall does cause a greater temperature drop than the other two rainfall systems. After further partitioning all samples by RH level preceding the rainfall, the relationship between hourly Ta variation and rainfall intensity became distinctive. When RH was below 70%, rainfall-induced cooling became more substantial and scaled linearly with event intensity, but when RH exceeded 70%, the rainfall cooling effect was generally restrained by the RH increase. A strong correlation between hourly Ta variation and RH level preceding the rainfall suggests the importance of RH on the rainfall cooling effect. [ABSTRACT FROM AUTHOR]

This study investigates the spatial–temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia–northeastern China can be identified, and the EPE variations over WS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar–Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British–Baikal Corridor (BBC)] pattern over 1987–2004 (2005–15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s. [ABSTRACT FROM AUTHOR]

This study investigates the spatial-temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia-northeastern China can be identified, and the EPE variations overWS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar-Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British-Baikal Corridor (BBC)] pattern over 1987-2004 (2005-15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products are commonly evaluated using gauge measurement, yet their regional evaluation and hydrological applicability have not been sufficiently studied, especially for dry basins. In this study, we evaluated the performance of four state-of-the-art remotely sensed precipitation products (CMORPH, GSMaP, IMERG, and PERSIANN-CDR) and their ensemble products (the reliability ensemble averaging and three-cornered hat methods) over the Heihe River basin, northwest China. Both direct evaluation using gauge measurement during 2001-19 and indirect evaluation using the Soil andWater Assessment Tool (SWAT) model during 2001-10 were conducted. Our results showed that 1) for point-to-pixel evaluation, GSMaP and IMERG products with high spatial resolution effectively captured the quantile distribution of gauge data; 2) compared to the spatially interpolated gauge data, all products underestimated the precipitation, among which GSMaP provided the closest interannual variability to the observations; 3) these products had better detection abilities upstream and during the rainy season, indicating that their performance was affected by the rain intensity-in particular, GSMaP exhibited the best ability; 4) the spatial patterns of individual products were inconsistent, while the ensemble products could reduce the bias with the gauge data; and 5) for hydrological modeling, streamflow simulation driven by GSMaP had the best performance, and the ensemble precipitation using the three-cornered hat method was better than that using the reliability ensemble averaging method. Collectively, these findings illustrated the reliability of GSMaP in representing the precipitation characteristics in similar arid areas and elucidated the advantages of using the three-cornered hat method. [ABSTRACT FROM AUTHOR]

We investigated the relationship between the frequency of occurrence of the Orinoco low-level jet (OLLJ) and hydroclimatic variables over northern South America. We use data from the ERA5 atmospheric reanalysis to characterize the spatial and temporal variability of the OLLJ in light of the low-level jet (LLJ) classification criteria available in the literature. An index for the frequency of occurrence of an LLJ was used, based on the hourly maxima of wind speed. The linkages among the OLLJ, water vapor flux, and precipitation were analyzed using a composite analysis. Our results show that during December–February (DJF), the OLLJ exhibits its maximum wind speed, with values around 8–10 m s−1. During DJF, the analysis shows how the OLLJ transports atmospheric moisture from the tropical North Atlantic Ocean. During this season, the predominant pathway of the OLLJ is associated with an area of moisture flux divergence located over northeastern South America. During June–August (JJA), an area of moisture flux convergence associated with the northernmost location of the ITCZ inhibits the entrance of moisture from northerlies. We also show that the occurrence of the OLLJ is associated with the so-called cross-equatorial flow. During DJF, the period of strongest activity of the OLLJ is associated with the northerly cross-equatorial flow and dry season, whereas during JJA the southerly cross-equatorial flow from the Amazon River basin predominates and contributes to the rainy season over the Orinoco region. [ABSTRACT FROM AUTHOR]

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here. [ABSTRACT FROM AUTHOR]

Moisture sources and their corresponding temperature and humidity are important for explosive extratropical cyclones' development regarding latent heating. To clarify the water origins and moisture transport processes within an explosive cyclone, we simulated an explosive cyclone migrating poleward across the Sea of Japan on 30 November 2014, by using an isotopic regional spectral model. In the cyclone's center area, a replacement of water origins occurred during the cyclone's development. During the early stage, the warm conveyor belt (WCB) transported large amounts of moisture from the East China Sea and Kuroshio into the cyclone's inner region. While in the deepening stage, the cold conveyor belt (CCB) and dry intrusion (DI) conveyed more moisture from the northwest Pacific Ocean and the Sea of Japan, respectively. Compared with the contribution of local moisture, that of remote moisture was dominant in the cyclone's center area. Regarding the water origins of condensation within the frontal system in the deepening stage, the northwest Pacific Ocean vapor, principally transported by the CCB, contributed 35.5% of the condensation in the western warm front. The East China Sea and Kuroshio moisture, conveyed by the WCB, accounted for 32.4% of the condensation in the cold and eastern warm fronts. In addition, condensation from the Sea of Japan, which was mainly triggered by the DI and induced by the topography, occurred on the west coast of the mainland of Japan and near the cyclone center. The spatial distribution of the isotopic composition in condensation and water vapor also supports the water-origin results. Significance Statement: We clarified the water origins with their corresponding temperature, humidity, isotopic composition, and moisture transport processes within an explosive extratropical cyclone. Condensation with low δ2H and d-excess in the western warm front was principally transported by the cold conveyor belt from the northwest Pacific Ocean with a cold and moist air mass, whereas condensation with high δ2H and d-excess in the cold and eastern warm fronts was mainly conveyed by the warm conveyor belt from the East China Sea and Kuroshio with a warm and humid air mass. These results point the way toward better understanding of where the moisture evaporates, how the moisture is transported, and where the moisture condenses, which can be used for model evaluation and weather forecasting in association with explosive extratropical cyclones. [ABSTRACT FROM AUTHOR]

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling. Significance Statement: To improve understanding of the effect of precipitation on floods, a machine-learning workflow is designed to scrutinize hydrological processes and is applied on a database of flood events over the United States. The model accurately reproduces observed maximum streamflow. It reflects physical hydrologic behavior that is consistent across basins, thereby addressing the challenge of regional modeling and improving upon traditional hydrological models. Rainfall spatial variability has a major influence on flood peak discharge, although basin geomorphology is the primary driver. This grants an improved understanding of the conditions under which floods are generated, allowing for better predictions and warning. [ABSTRACT FROM AUTHOR]

As raindrops fall from the cloud base to the ground, evaporation below those clouds affects the rain's isotope ratio, reduces precipitation in arid areas, and impacts the local climate. Therefore, in arid areas with scarce water resources and fragile ecological environments, the below-cloud evaporation is an issue of great concern. Based on 406 event-based precipitation samples collected from nine stations in the Shiyang River basin (SRB) in the northwest arid area, global meteorological water line (GMWL) and local meteorological water line (LMWL) are compared, and the Stewart model is used to study the effect of spatial and temporal variation of below-cloud evaporation on isotope values in different geomorphic units at the SRB. Furthermore, factors influencing below-cloud evaporation are analyzed. The results show that 1) the change of d-excess (Δd) in precipitation at the SRB and the residual ratio of raindrop evaporation (f) vary in time and space. With regard to temporal variation, the intensity of below-cloud evaporation is described by summer < autumn < winter < spring. Regarding spatial variation, the below-cloud evaporation in mountain areas is weaker than in oases and deserts. The intensity of below-cloud evaporation in mountain areas increases with decreasing altitude, and the below-cloud evaporation in oasis and desert areas is affected by local climatic conditions. 2) Below-cloud evaporation is also affected by local transpiration evaporation, especially around reservoirs. Reservoirs increase the relative humidity of the air nearby, weakening below-cloud evaporation. This study deepens our understanding of the water cycle process in arid areas. [ABSTRACT FROM AUTHOR]

Three satellite gridded daily precipitation datasets—PERSIANN-CDR, GPCP, and CMORPH—that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20 years to over 35 years. The products intercomparisons are performed at various temporal (daily to annual) resolutions and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in situ datasets from the Global Historical Climatology Network (GHCN-Daily) and the U.S. Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio, among others). All SPPs were found to underestimate extreme rainfall (i.e., above the 90th percentile) from about −20% for CMORPH to −50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned, from the long-term qualitative analysis of hydroclimatological properties to the quantification of daily extreme events, for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications. [ABSTRACT FROM AUTHOR]

On 12 and 13 September 2019, widespread flash flooding caused devastating effects across eastern Spain. Within the framework of the HyMeX program, this study examines predictability of the long-lasting heavy precipitation episode (HPE) conducive to flash flooding. Aset of short-range, convection-permitting ensemble prediction systems (EPSs) is built to cope with different sources of meteorological uncertainty. Specifically, the performance of an ensemble Kalman filter, tailored bred vectors, and stochastic model parameterizations is compared to more standard ensemble generation techniques such as dynamical downscaling and multiple physics. Results indicate EPSs focusing on sampling model uncertainties related to parameterization of subgrid processes are skillful, especially when deep convection and its interaction with complex orography are important. Furthermore, representation of small-scale thermodynamical aspects is improved through data assimilation, leading to an enhanced forecasting skill as well. Nevertheless, predictability remains relatively low at the catchment scale in terms of exceedance probabilities in cumulative precipitation and peak discharge. The analysis presented herein could serve as a basis for the future implementation of real-time flash flood warning systems based on skillful meteorological EPSs over small-to-medium, semiarid watersheds in eastern Spain and, by extension, over the floodprone western Mediterranean region. [ABSTRACT FROM AUTHOR]

Accurate snowfall measurement is challenging because it depends on the precipitation gauge used, meteorological conditions, and the precipitation microphysics. Upstream of weighing gauges, the flow field is disturbed by the gauge and any shielding used usually creates an updraft, which deflects solid precipitation from falling in the gauge, resulting in significant undercatch. Wind shields are often used with weighing gauges to reduce this updraft, and transfer functions are required to adjust the snowfall measurements to consider gauge undercatch. Using these functions reduces the bias in precipitation measurement but not the root-mean-square error (RMSE). In this study, the accuracy of the Hotplate precipitation gauge was compared to standard unshielded and shielded weighing gauges collected during the WMO Solid Precipitation Intercomparison Experiment program. The analysis performed in this study shows that the Hotplate precipitation gauge bias after wind correction is near zero and similar to wind corrected weighing gauges. The RMSE of the Hotplate precipitation gauge measurements is lower than weighing gauges (with or without an Alter shield) for wind speeds up to 5 m s−1, the wind speed limit at which sufficient data were available. This study shows that the Hotplate precipitation gauge measurement has a low bias and RMSE due to its aerodynamic shape, making its performance mostly independent of the type of solid precipitation. [ABSTRACT FROM AUTHOR]

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models' IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the "true" underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods. [ABSTRACT FROM AUTHOR]

The collection efficiency of a typical precipitation gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The high scatter in the collection efficiency for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado, during the WMO Solid Precipitation Intercomparison Experiment (SPICE). Particle diameter and fall speed data from a laser disdrometer were used to show that the scatter in the collection efficiency can be reduced by considering the fall speed of solid precipitation particles. The collection efficiency was divided into two classes depending on the measured mean-event particle fall speed during precipitation events. Slower-falling particles were associated with a lower collection efficiency. A new transfer function (i.e., the relationship between collection efficiency and other meteorological variables, such as wind speed or air temperature) that includes the fall speed of the hydrometeors was developed. The root-mean-square error of the adjusted precipitation with the new transfer function with respect to a weighing gauge placed in a double fence intercomparison reference was lower than using previously developed transfer functions that only consider wind speed and air temperature. This shows that the measured fall speed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency. [ABSTRACT FROM AUTHOR]

The Radar Applications in Northern Scotland (RAiNS) experiment took place from February to August 2016 near Inverness, Scotland. The campaign was motivated by the need to provide enhanced weather radar observations for hydrological applications for the Inverness region. Here we describe the campaign in detail and observations over the summer period of the campaign that show the improvements that high-resolution polarimetric radar observations may have on quantitative precipitation estimates in this region compared to concurrently generated operational radar quantitative precipitation estimates (QPEs). We further provide suggestions of methods for generating QPE using dual-polarization X-band radars in similar regions. [ABSTRACT FROM AUTHOR]

Groundwater is rapidly depleting in India primarily because of pumping for irrigation. However, the crucial role of crop growth at annual and seasonal time scales in groundwater storage variability remains mostly unexplored. Using the data from the Gravity Recovery Climate Experiment (GRACE) satellites and well observations, we show that crop growth is negatively correlated with groundwater storage at annual and seasonal time scales in north India. Precipitation is positively associated with groundwater storage variability at the yearly time scale in north-central India (NCI) and south India (SI). In contrast, precipitation is negatively correlated with groundwater storage from the GRACE satellites in northwest India (NWI). The negative correlation between precipitation and groundwater from the GRACE in NWI is primarily due to groundwater depletion due to anthropogenic pumping from deep aquifers. Precipitation and groundwater storage from the well observations are positively correlated in all the three regions, indicating the influence of precipitation on shallow aquifers. Analysis of the two main crop growing seasons (Rabi and Kharif) showed that crop growth is negatively related to groundwater storage in both Kharif (June–September) and Rabi seasons in north India (NWI and NCI). Groundwater contributes more than precipitation in NCI during the Kharif season and in NWI and SI during the Rabi season. Granger's causality test showed that groundwater is a significant contributor to crop growth in NWI and NCI in both Kharif and Rabi seasons. Our results highlight the need for agricultural water management in both the crop growing seasons in north India for reducing the rapid groundwater depletion. [ABSTRACT FROM AUTHOR]

The evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China. [ABSTRACT FROM AUTHOR]

This study quantifies the contribution of atmospheric rivers (ARs) to annual and extreme river runoff and evaluates the relationships between watershed characteristics and AR-related maximum river runoff across British Columbia and southeastern Alaska (BCSAK). Datasets used include gauged runoff from 168 unregulated watersheds, topographic characteristics of those watersheds, a regionalARcatalog, and integrated vapor transport fields for water years (WYs) 1979-2016. ARs contribute ;22% of annual river runoff along the Coast and Insular Mountains watersheds, which decreases inland to ;11% in the watersheds of the Interior Mountains and Plateau. Average association between ARs and annual maximum river runoff attains .80%, .50%, and ,50% along the watersheds of the western flanks of the Coast Mountains, the Interior Mountains, and Interior Plateau, respectively. There is no significant change in AR-related extreme annual maximum runoff across BCSAK during 1979-2016. AR conditions occur during 25 out of 32 of the flood-related natural disasters in British Columbia duringWYs 1979-2016. AR-related annual maximum runoff magnitude is significantly higher than non-AR-related annual maximum runoff for 30% of the watersheds studied. Smaller and steeper watersheds closer to the coast are more susceptible to AR-related annual maximum runoff than their inland counterparts. These results illustrate the importance ofARactivity as a major control for the distribution of peak runoff in BCSAK. This work provides insights on the hydrological response of watersheds of northwestern North America to landfalling ARs that may improve flood risk assessment and disaster management in this region. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the sevenU.S. National ClimateAssessment regions.ARs aremost frequent in the autumn and winter in theWest, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastwarddirected moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts. [ABSTRACT FROM AUTHOR]