Showing papers in "Journal of Hydrometeorology in 2020"

Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China

Abstract: This study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized preci...

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

Abstract: Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications includ...

Comparison of Rainfall Products over Sub-Saharan Africa

Abstract: An ever-increasing number of rainfall estimates is available. They are used in many important applications such as flood/drought monitoring, water management, or climate monitoring. Such da...

Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index

Abstract: Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmenta...

Evaluation of a Convection-Permitting Modeling of Precipitation over the Tibetan Plateau and Its Influences on the Simulation of Snow-Cover Fraction

Abstract: Precipitation is a critical input to land surface and hydrology modeling and prediction. Dynamical downscale modeling has added value to representing precipitation, when compared with the performance of coarse-resolution reanalysis and global climate models, over the Tibetan Plateau (TP). Convection-permitting modeling (CPM) may even outperform dynamical downscale models (DDMs). In this study, 4-km CPM results were compared to 28-km DDM results for a snow season (1 October 2013–31 May 2014) over the TP. The CPM- and DDM-simulated precipitation, as well as three merged gridded precipitation datasets, were evaluated against in situ observations below 4800 m. The five precipitation datasets (CPM, DDM, CMFD, COPRPH, and TRMM) showed large differences over the TP with underestimation of TRMM and overestimation of CPM and DDM compared to observations. The most significant difference occurred in the Brahmaputra Grand Canyon. Given the substantial uncertainty in observed precipitation at high mountains, snow cover simulated by a high-resolution land data assimilation system was used to indirectly evaluate the above precipitation data using MODIS observations. Simulated snow-cover fraction was greatly underestimated using all the merged precipitation datasets. However, simulations using the DDM- and CPM-generated precipitation as input outperformed those using other gridded precipitation data, showing lower biases, higher pattern correlations, and closer probability distribution functions than runs driven by the merged precipitation. The findings of this study generally support the assumption that high-resolution CPM-produced precipitation has an added value for use in land surface and hydrology simulations in high-mountain regions without reliable in situ precipitation observations.

Can the GPM IMERG Final Product Accurately Represent MCSs’ Precipitation Characteristics over the Central and Eastern United States?

Abstract: Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An acc...

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Abstract: This study attempts to assess the validity of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) products across Iran. Six IMERG precipitation products (I...

Spatial Distribution of Global Landscape Evaporation in the Early Twenty-First Century by Means of a Generalized Complementary Approach

Abstract: A generalized implementation of the complementary principle was applied to estimate global land surface evaporation and its spatial distribution. The single parameter in the method was cali...

Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

Abstract: Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.

The contribution of rain gauges in the calibration of the IMERG product: results from the first validation over Spain

Abstract: After 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Sp...

Increased Drought Risk in South Asia under Warming Climate: Implications of Uncertainty in Potential Evapotranspiration Estimates

Abstract: Observed and projected changes in potential evapotranspiration (PET) and drought are not well constrained in South Asia. Using five PET estimates (Thornthwaite: PET-TH, Hargreaves-Samani: P...

Characterizing Surface Albedo of Shallow Fresh Snow and Its Importance for Snow Ablation on the Interior of the Tibetan Plateau

Abstract: Snow depth on the interior of Tibetan Plateau (TP) in state-of-the-art reanalysis products is almost an order of magnitude higher than observed. This huge bias stems primarily from excessiv...

PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset.

Abstract: This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15-60 min). It is intended to supersede the PERSIANN-Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm's fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017-18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

Abstract: This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

Evaluation of a physics-based tropical cyclone rainfall model for risk assessment

Abstract: Heavy rainfall generated by landfalling tropical cyclones (TCs) can cause extreme flooding. A physics-based TC rainfall model (TCRM) has been developed and coupled with a TC climatology model to study TC rainfall climatology. In this study, we evaluate TCRM with rainfall observations made by satellite (of North Atlantic TCs from 1999 to 2018) and radar (of 36 U.S. landfalling TCs); we also examine the influence on the rainfall estimation of the key input to TCRM—the wind profile. We found that TCRM can simulate relatively well the rainfall from TCs that have a coherent and compact structure and limited interaction with other meteorological systems. The model can simulate the total rainfall from TCs well, although it often overestimates rainfall in the inner core of TCs, slightly underestimates rainfall in the outer regions, and renders a less asymmetric rainfall structure than the observations. It can capture rainfall distribution in coastal areas relatively well but may underestimate rainfall maximums in mountainous regions and has less capability to accurately simulate TC rainfall in higher latitudes. Also, it can capture the interannual variability of TC rainfall and averaged features of the time series of TC rainfall but cannot accurately reproduce the probability distribution of short-term (1 h) rainfall. Among the tested theoretical wind profile inputs to TCRM, a complete wind profile that accurately describes the wind structure in both the inner ascending and outer descending regions of the storm is found to perform the best in accurately generating various rainfall metrics.

A Multiscale, Hydrometeorological Forecast Evaluation of National Water Model Forecasts of the May 2018 Ellicott City, Maryland, Flood

Abstract: The NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United St...

Two Different Methods for Flash Drought Identification: Comparison of Their Strengths and Limitations

Abstract: Flash droughts are extreme phenomena that have been identified using two different approaches. The first approach identifies these events based on unusually rapid intensification rates, whe...

A Dual-Polarization Radar Synthetic QPE for Operations

Abstract: A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation (A), specific differential phase (...

A Process-Based Validation of GPM IMERG and Its Sources Using a Mesoscale Rain Gauge Network in the West African Forest Zone

Abstract: 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 (I...

Assessment of Extremes in Global Precipitation Products: How Reliable Are They?

Abstract: Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.

Assessing Tropical Cyclones' Contribution to Precipitation over the Eastern United States and Sensitivity to the Variable-Resolution Domain Extent

Abstract: Tropical cyclones (TCs) can subject an area to heavy precipitation for many hours, or even days, worsening the risk of flooding, which creates dangerous conditions for residents of the Unit...

Global Estimates of Land Surface Water Fluxes from SMOS and SMAP Satellite Soil Moisture Data

Abstract: In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water ...

Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity

Abstract: Atmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to inves...

Performance of Multiple Satellite Precipitation Estimates over a Typical Arid Mountainous Area of China: Spatiotemporal Patterns and Extremes

Abstract: Precipitation in arid mountainous areas is characterized by low rainfall intensity and large spatial heterogeneity, which challenges satellite-based monitoring by the spaceborne sensors. This study aims to comparatively evaluate the detection ability of spatiotemporal patterns and extremes of rainfall by a range of mainstream satellite precipitation products [TMPA, Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and PERSIANN-Climate Data Record (PERSIANN-CDR)] over a typical arid mountainous basin of China, benchmarking against rain gauge data from 2000 to 2015. Results showed that satellite precipitation estimates had relatively low accuracy at the daily scale, while a significant improvement of correlation coefficient (CC;>0.6) and a significant reduction of relative root-mean-square error (RRMSE;, 1.0) were found as time scale increases beyond the monthly scale. CHIRPS tended to overestimate the gauge precipitation with positive relative bias (RB), while the negative RBvalues for TMPAand PERSIANN-CDR indicated there was an underestimation. CHIRPS had the most similar spatial pattern and slope trends of the seasonal precipitation and interannual variations of annual precipitation with gauge observations. With the increase in rainfall rates, the probability of detection (POD) and critical success index (CSI) were reduced and the false alarm ratio (FAR) was increased significantly, demonstrating the limited capability for all the three satellite products for detecting heavy rainfall events. CHIRPS showed the best performance in detecting rainfall extremes compared to TMPA and PERSIANN-CDR, evidenced by the largerCSI values and similar extreme rainfall indices obtained from gauge records. This study provides valuable guidance for choosing satellite precipitation products instead of gauge observations for rainfall monitoring (especially rainfall extremes) and agricultural production management over arid mountainous area. (Less)

GRACE improves seasonal groundwater forecast initialization over the U.S.

Abstract: We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the U.S., focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003-2016 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth-to-water-table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts towards an improved U.S. drought monitor and forecast system.

Quantifying Precipitation Efficiency and Drivers of Excessive Precipitation in Post-Landfall Hurricane Harvey

Abstract: Hurricane Harvey produced unprecedented widespread rainfall amounts over 1000 mm in portions of southeast Texas, including Houston, from 26 to 31 August 2017. The highly efficient and prolo...

Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

Abstract: Merged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide highe...

Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity

Abstract: Adjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed...

Undercatch adjustments for tipping bucket gauge measurements of solid precipitation

Abstract: Heated tipping-bucket (TB) gauges are used broadly in national weather monitoring networks, but their performance for the measurement of solid precipitation has not been well characterized....

Reconstructing Flood Events in Mediterranean Coastal Areas Using Different Reanalyses and High-Resolution Meteorological Models

Abstract: Reliable reanalysis products can be exploited to drive mesoscale numerical models and generate high-resolution reconstructions of high-impact weather events. Within this framework, regional weather and climate models may greatly benefit from the recent release of the ERA5 product, an improvement to the ERA-Interim dataset. In this study, two different convection-permitting models driven by these two reanalysis datasets are used to reproduce three heavy precipitation events affecting a Mediterranean region. Moreover, different sea surface temperature (SST) initializations are tested to assess how higher-resolution SST fields improve the simulation of high-impact events characterized by strong air–sea interactions. Finally, the coupling with a distributed hydrological model allows evaluating the impact at the ground, specifically assessing the possible added value of the ERA5 dataset for the high-resolution simulation of extreme hydrometeorological events over the Calabria region (southern Italy). Results, based on the comparison against multiple-source precipitation observations, show no clear systematic benefit to using the ERA5 dataset; moreover, intense convective activity can introduce uncertainties masking the signal provided by the boundary conditions of the different reanalyses. The effect of the high-resolution SST fields is even more difficult to detect. The uncertainties propagate and amplify along the modeling chain, where the spatial resolution increases up to the hydrological model. Nevertheless, even in very small catchments, some of the experiments provide reasonably accurate results, suggesting that an ensemble approach could be suitable to cope with uncertainties affecting the overall meteo-hydrological chain, especially for small catchments.