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Daniel Whitesel
,
Rezaul Mahmood
,
Christopher Phillips
,
Joshua Roundy
,
Eric Rappin
,
Paul Flanagan
,
Joseph A. Santanello Jr.
,
Udaysankar Nair
, and
Roger Pielke Sr.

Abstract

Land-use land-cover change affects weather and climate. This paper quantifies land–atmosphere interactions over irrigated and nonirrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify land–atmosphere interactions as they relate to convection. They include the convective triggering potential (CTP), the low-level humidity index (HIlow), and the lifting condensation level (LCL) deficit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISSs) for Rogers Farm and York Airport along with soundings launched from the three Doppler on Wheels (DOW) sites. Each metric was categorized by intensive observation period (IOP), cloud cover, and time of day. Results show that with higher CTP, lower HIlow, and lower LCL deficit, conditions were more favorable for convective development over irrigated land use. When metrics were grouped and analyzed by IOP, compared to nonirrigated land use, HIlow was found to be lower for irrigated land use, suggesting favorable conditions for convective development. Furthermore, when metrics were grouped and analyzed by clear and nonclear days, CTP values were higher over irrigated cropland than nonirrigated land use. In addition, compared to nonirrigated land use, the LCL deficit during the peak growing season was lower over irrigated land use, suggesting a favorable condition for convection. It is found that with the transition from the early summer to the mid/peak summer and increased irrigation, the environment became more favorable for convective development over irrigated land use. Finally, it was found that regardless of background atmospheric conditions, irrigated land use provided a favorable environment for convective development.

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Florence L. Beaudry
,
Stéphane Bélair
,
Julie M. Thériault
,
Dikra Khedhaouiria
,
Franck Lespinas
,
Daniel Michelson
,
Pei-Ning Feng
, and
Catherine Aubry

Abstract

The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Open access
Hongping Gu
,
Wei Zhang
, and
Robert Gillies

Abstract

The Great Salt Lake (GSL) is a shallow terminal lake located in northern Utah, United States. Over the years, the water extent of the GSL has undergone substantial reduction due to water diversions and a changing climate—in particular rising temperatures. However, the potential impacts of the shrinking GSL water body on the local hydroclimate system are poorly understood. In this study, we utilized the Weather Research and Forecasting Model, version 4.2, coupled with a lake model to simulate a series of high-resolution numerical experiments; these experiments aimed to assess the effect of varying lake areal extents on a storm event that occurred on 6 June 2007. The results revealed a systematic decline in the quantity of precipitation over the GSL and downwind regions with declining areal coverage. In the event of complete disappearance, the regional average precipitation would experience an approximate 50% reduction relative to its 2004 base lake extent; this decrease is principally attributed to a diminished water vapor flux and moist static energy (MSE) above the lake. The research underscores the consequences of a shrinking GSL, not just for precipitation delivery downstream but that of a negative feedback loop within the hydroclimatic system of the GSL basin, i.e., water flow reductions into the basin.

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Xinxin Xie
,
Xiao Xiao
,
Jieying He
,
Pablo Saavedra Garfias
,
Tiejian Li
,
Xiaoyu Yu
,
Songyan Gu
, and
Yang Guo

Abstract

This study investigates precipitation observed by a set of collocated ground-based instruments in Zhuhai, a coastal city located at the southern tip of the Pearl River Delta of Guangdong Province in South China. Seven months of ground-based observations from a tipping-bucket rain gauge (RG), two laser disdrometers (PARSIVEL and Present Weather Sensor 100 (PWS)], and a vertically pointing Doppler Micro Rain Radar-2 (MRR), spanning from December 2021 to July 2022, are statistically evaluated to provide a reliable reference for China’s spaceborne precipitation measurement mission. Rainfall measurement discrepancies are found between the instruments though the collocated deployment mitigates uncertainties originating from spatial/temporal variabilities of precipitation. The RG underestimates hourly rain amounts at the observation site, opposite to previous studies, leading to a percent bias (Pbias) of 18.2% of hourly rain amounts when compared to the PARSIVEL. With the same measurement principle, the hourly accumulated rain between the two laser disdrometers has a Pbias of 15.3%. Discrepancies between MRR and disdrometers are assumed to be due to different temporal/spatial resolution, instrument sensitivities, and observation geometry, with a Pbias of mass-weighted mean diameter and normalized intercept parameter of gamma size distribution less than 9%. The vertical profiles of drop size distribution (DSD) derived from the MRR are further examined during extreme rainfalls in the East Asian monsoon season (May, June, and July). Attributed to the abundant moisture which favors the growth of raindrops, coalescence is identified as the predominant effective process, and the raindrop mass-weighted mean diameter increases by 33.7% when falling from 2000 to 600 m during the extreme precipitation event in May.

Significance Statement

The performance and reliability of ground-based observations during precipitation scenarios are evaluated over the coastal area of South China, in preparation for China’s spaceborne precipitation measurement mission. A comparison study, which is carried out to assess the accuracy of rainfall and drop size distribution (DSD), demonstrates that the observation results are relatively reliable though discrepancies between the instruments still exist, while the accompanying microphysical process during extreme precipitation can be quantified with profiling capabilities at the observatory. An accurate and reliable rainfall characterization over the coastal region in South China can contribute to the validation of satellite rainfall products and provide further insights into the microphysical parameterization schemes during extreme precipitation.

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Yuting Yang
,
Xiaopeng Cui
,
Ying Li
,
Lijun Huang
, and
Jia Tian

Abstract

The northeast cold vortex (NECV) is an essential system in the northeast region (NER) of China. Understanding the moisture source and associated transport characteristics of NECV rainstorms is the key to the knowledge of its mechanisms. In this study, we focus on two NECV rainstorm centers during the warm season (May–September) from 2008 to 2013. The Flexible Particle (FLEXPART) model and quantitative contribution analysis method are applied to reveal the moisture sources and their quantitative contribution. The results demonstrate that for the northern NECV rainstorm center (R1), Northeast Asia (35.66%) and east-central China and its coastal regions (29.14%) make prominent moisture contributions, followed by R1 (11.37%), whereas east-central China and its coastal regions (45.16%), the southern NECV rainstorm center itself (R2, 17.90%), and the northwest Pacific (10.24%) principally contribute to R2. Moisture uptake in Northeast Asia differs between R1 and R2, which could serve as one of the vital indicators to judge where the NECV rainstorm falls in NER. Moisture from the Arabian Sea, the Bay of Bengal, and the South China Sea suffers massive en route loss, although these sources’ contribution and uptake are positively correlated with the intensity and scale of NECV rainstorms in the two centers. There exists intermonth and geographical variability in NECV rainstorms when the main moisture source region contributes the most. Regulated by the atmospheric circulation and the East Asian summer monsoon, the particle trajectories and source contributions of NECV rainstorms vary from month to month. Sources’ contribution also turns out to be diverse in the overall warm season.

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Abby Hutson
,
Ayumi Fujisaki-Manome
, and
Brent Lofgren

Abstract

The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated.

Significance Statement

Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Open access
Jessica R. P. Sutton
,
Dalia Kirschbaum
,
Thomas Stanley
, and
Elijah Orland

Abstract

Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards.

Significance Statement

Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

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Kyle Lesinger
,
Di Tian
, and
Hailan Wang

Abstract

Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ET o ), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ET o and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ET o forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability.

Significance Statement

Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

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Reyhaneh Rahimi
,
Praveen Ravirathinam
,
Ardeshir Ebtehaj
,
Ali Behrangi
,
Jackson Tan
, and
Vipin Kumar

Abstract

This paper presents a deep supervised learning architecture for 30-min global precipitation nowcasts with a 4-h lead time. The architecture follows a U-Net structure with convolutional long short-term memory (ConvLSTM) cells empowered by ConvLSTM-based skip connections to reduce information loss due to the pooling operation. The training uses data from the Integrated Multi-satellitE Retrievals for GPM (IMERG) and a few key drivers of precipitation from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (<1.6 mm h−1), while the classification network can outperform the regression counterpart for nowcasting of high-intensity precipitation (>8 mm h−1), in terms of the critical success index (CSI). It is uncovered that including the forecast variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multiscale analysis, in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful for precipitation rate above 1 mm h−1 at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4 mm h−1, only the classification network remains FSS skillful on scales greater than 50 km within a 2-h lead time.

Significance Statement

This study presents a deep neural network architecture for global precipitation nowcasting with a 4-h lead time, using sequences of past satellite precipitation data and simulations from a numerical weather prediction model. The results show that the nowcasting machine can improve short-term predictions of high-intensity global precipitation. The research outcomes will enable us to expand our understanding of how modern artificial intelligence can improve the predictability of extreme weather and benefit flood early warning systems for saving lives and properties.

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Hoang Tran
,
Yilin Fang
,
Zeli Tan
,
Tian Zhou
, and
L. Ruby Leung

Abstract

The Lower Mississippi River basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here, we study the impacts of land-cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface–subsurface hydrologic model, Energy Exascale Earth System Model (E3SM) Land Model coupled with the three-dimensional ParFlow subsurface flow model (ELM-ParFlow), we simulate the effects of land-cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land-cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land-cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land-use, land-cover, and hydrologic processes in coastal regions.

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