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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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Paolo Reggiani
and
Oleksiy Boyko

Abstract

We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), high mountain Asia (HMA). A process-based hydrological model at 0.5-km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records, and reanalyses over the 2008–15 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilistic processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate.

Significance Statement

We show how to use different precipitation estimates, like computer simulations of weather or satellite observations, in conjunction with all available ground measurements in regions with generally poor meteorological and flow measurement infrastructure. We demonstrate how it is possible to retrieve past unobserved river flows using these estimates in combination with a hydrological computer model for streamflow simulations. The method can help us to better understand the hydrology of poorly gauged regions that play an important role in the distribution of water resources and can be affected by future changes. We applied the method to a large transboundary river basin in China. This basin holds water needed by large, densely populated regions of India that may become water constrained by warmer climate.

Open access
Xiaogang Ma
,
Kun Yang
,
Binbin Wang
,
Zhaoguo Li
,
Lazhu
,
Hui Lu
,
Xiangnan Yao
, and
Xin Chen

Abstract

Skin cooling, wherein the surface temperature of a water body T skin is lower than the temperature below the surface, is a widespread phenomenon. Previous studies have almost ignored this effect on the Tibetan Plateau (TP), despite the presence of thousands of lakes on the TP and the fact that extraordinary solar heating leads to very strong energy exchanges on the lake surfaces. This study utilizes in situ observations and MODIS-derived T skin data at Lake Nam Co, one of the largest lakes on the TP, to quantify the skin cooling effect. The observed nighttime skin cooling is approximately 0.52°C on average, with the maximum of about 1°C, during the lake water turnover period (from October to mid-November), which obviously surpasses reported values for oceans (less than 0.4°C). To understand the impact of the skin cooling on the lake thermal processes, a skin cooling parameterization is validated and incorporated into the WRF-lake model. Simulations with the updated model show that accounting for the skin cooling process systematically lowers sensible and latent heat fluxes by a few watts per square meter, which yields an increase in water temperature by 0.45°C at the end of December and may delay the onset of lake freeze. Finally, we show that the inclusion of the skin cooling process in a lake model needs simultaneous adjustment of the parameterization of heat/water vapor transfer.

Significance Statement

Skin cooling is a widespread phenomenon for a water surface, and its intensity depends on the energy flux exchange of the water surface. The Tibetan Plateau possesses the presence of thousands of lakes, but early studies have ignored the skin cooling effect. We found that the nighttime skin cooling magnitude during the lake water turnover period in this region obviously exceeds reported values for oceans, due to the strong surface energy exchange in the Tibetan Plateau. Neglecting the skin cooling process may lead to systematic overestimation of turbulent heat fluxes and underestimation of water temperature. We highlight that accounting for this skin cooling process is crucial to select appropriate parameterization schemes for heat/water vapor transfer in lake thermal process modeling.

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Linda Bogerd
,
Chris Kidd
,
Christian Kummerow
,
Hidde Leijnse
,
Aart Overeem
,
Veljko Petkovic
,
Kirien Whan
, and
Remko Uijlenhoet

Abstract

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands—a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Significance Statement

Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

Open access
Huancui Hu
,
L. Ruby Leung
,
Zhe Feng
, and
James Marquis

Abstract

Moisture recycling, the contribution of local evapotranspiration (ET) to precipitation, has been studied using bulk models assuming a well-mixed atmosphere. The latter is inconsistent with a climatologically stratified atmosphere that slants across latitudes. Reconciling the two views requires an understanding of overturning associated with different weather systems. In this study, we aim to better understand moisture recycling associated with mesoscale convective systems (MCSs). Using a convection-permitting WRF simulation equipped with water vapor tracers (WRF-WVT), we tag moisture from terrestrial ET in the U.S. Southern Great Plains during May 2015, when more than 20 MCS events occurred and produced significant precipitation and flooding. Water budget analysis reveals that approximately 76% of terrestrial ET is advected away from the region while the remaining 24% of terrestrial ET is “pumped” upward within the region, accounting for 12% of precipitation. Moisture recycling peaks during early night hours (1800–2400 LT) due to the mixing of the daytime accumulated ET by active convection. By focusing on five “diurnally driven” MCSs with less large-scale circulation influence than other MCSs during the same period, we find an upright pumping of terrestrial ET at the MCS initiation and development stages, which diverges into two branches during the MCS mature and decaying stages. One branch in the upper level advects the ET-sourced moisture downstream, while the other branch in the mid-to-upper level contributes to the trailing precipitation upstream. Overall, our analysis depicts a pumping mechanism associated with MCSs that mixes local ET vertically, highlighting its specific contributions to enhancing convective precipitation processes.

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