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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 early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG), provides NRT 0.1° and 30-minute precipitation estimates across the globe with only a 4-hour latency. This study was an evaluation of the GPM IMERG version 6 level-3 Early Run 30-minute 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-minute precipitation from gauges and 30-minute precipitation from the IMERG multi-satellite constellation. False positive and false negative precipitation events were identified and analyzed to determine 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.

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Guangming Zheng
,
Stephanie Schollaert Uz
,
Pierre St-Laurent
,
Marjorie A. M. Friedrichs
,
Amita Mehta
, and
Paul M. DiGiacomo

Abstract

Seasonal hypoxia is a recurring threat to ecosystems and fisheries in the Chesapeake Bay. Hypoxia forecasting based on coupled hydrodynamic and biogeochemical models has proven useful for many stakeholders, as these models excel in accounting for the effects of physical forcing on oxygen supply, but may fall short in replicating the more complex biogeochemical processes that govern oxygen consumption. Satellite-derived reflectances could be used to indicate the presence of surface organic matter over the Bay. However, teasing apart the contribution of atmospheric and aquatic constituents from the signal received by the satellite is not straightforward. As a result, it is difficult to derive surface concentrations of organic matter from satellite data in a robust fashion. A potential solution to this complexity is to use deep learning to build end-to-end applications that do not require precise accounting of the satellite signal from atmosphere or water, phytoplankton blooms, or sediment plumes. By training a deep neural network with data from a vast suite of variables that could potentially affect oxygen in the water column, improvement of short-term (daily) hypoxia forecast may be possible. Here we predict oxygen concentrations using inputs that account for both physical and biogeochemical factors. The physical inputs include wind velocity reanalysis information, together with 3D outputs from an estuarine hydrodynamic model, including current velocity, water temperature, and salinity. Satellite-derived spectral reflectance data are used as a surrogate for the biogeochemical factors. These input fields are time series of weekly statistics calculated from daily information, starting 8 weeks before each oxygen observation was collected. To accommodate this input data structure, we adopted a model architecture of long short-term memory networks with 8 time steps. At each time step, a set of convolutional neural networks are used to extract information from the inputs. Ablation and cross validation tests suggest that among all input features, the strongest predictor is the 3D temperature field, with which the new model can outperform the state-of-the-art by ∼20% in terms of median absolute error. Our approach represents a novel application of deep learning to address a complex water management challenge.

Open access
Yevgenii Rastigejev
and
Sergey A. Suslov

Abstract

This study focuses on the influence of the sea spray polydispersity on the vertical transport of momentum in a turbulent marine atmospheric boundary layer in high-wind conditions of a hurricane. The Eulerian multifluid model treating air and spray droplets of different sizes as interacting inter-penetrating continua is developed and its numerical solutions are analyzed. Several droplet size distribution spectra and correlation laws relating wind speed and spray production intensity are considered. Polydisperse model solutions have confirmed the difference between the roles small and large spray droplets play in modifying the turbulent momentum transport that have been previously identified by monodisperse spray models. The obtained results have also provided a physical explanation for the previously unreported phenomenon of the formation of thin low-eddy-viscosity “sliding” layers in strongly turbulent boundary layer flows laden with predominantly fine spray.

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Erik S Krueger
,
Tyson E Ochsner
, and
B Wade Brorsen

Abstract

The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the United States Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic drought. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to (1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, (2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and (3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, USA, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

Open access
Tingting Zhu
and
Jin-Yi Yu

Abstract

Utilizing a 2200-year CESM1 pre-industrial simulation, this study examines the influence of property distinctions between single-year (SY) and multi-year (MY) La Niñas on their respective impacts on winter surface air temperatures across mid-to-high latitude continents in the model, focusing on specific teleconnection mechanisms. Distinct impacts were identified in four continent sectors: North America, Europe, Western Siberia (W-Siberia), and Eastern Siberia (E-Siberia). The typical impacts of simulated SY La Niña events are featured with anomalous warming over Europe and W&E-Siberia and anomalous cooling over North America. Simulated MY La Niña events reduce the typical anomalous cooling over North America and the typical anomalous warming over W&E-Siberia but intensify the typical anomalous warming over Europe. The distinct impacts of simulated MY La Niñas are more prominent during their first winter than during the second winter, except over W-Siberia, where the distinct impact is more pronounced during the second winter. These overall distinct impacts in the CESM1 simulation can be attributed to the varying sensitivities of these continent sectors to the differences between MY and SY La Niñas in their intensity, location, and induced sea surface temperature anomalies in the Atlantic Ocean. These property differences were linked to the distinct climate impacts through the Pacific North America, North Atlantic Oscillation, Indian Ocean-induced wave train, and Tropical North Atlantic-induced wave train mechanisms. The modeling results are then validated against observations from 1900 to 2022 to identify disparities in the CESM1 simulation.

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Daniel Galea
,
Kevin Hodges
, and
Bryan N. Lawrence

Abstract

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.

Open access
Fang Zhou
,
Siseho Christonette Siseho
,
Minghong Liu
,
Dapeng Zhang
, and
Haoxin Zhang

Abstract

Focusing on summer precipitation over the Tibetan Plateau (TP), this study mainly investigates the joint impacts of the North African and the Western Pacific subtropical highs (i.e., NASH and WPSH) through examining circulation and moisture anomalies. Results show that there are several boundary combination types of the two subtropical highs. The anomalous vertical motion with sufficient moisture transport under different boundary types plays the dominant role in TP precipitation anomaly. When the WPSH strengthens westward approaching to the TP, it can transport water vapor northward from Northwest Pacific and North Indian Oceans to the south edge of the TP and induce ascending motion over the southeastern TP, contributing to more precipitation there. When the NASH enhances and extends eastward, it can transport water vapor eastward from North Atlantic Ocean to the southwest eastern TP and give rise to ascending motion there, inducing positive precipitation anomaly over the southwest eastern TP. When the two subtropical highs simultaneously intensify and extend to the TP, water vapor can be transported to the TP widely from the North Atlantic Ocean, the North Indian Ocean and the northwest Pacific Ocean with the strengthening of the westerly, resulting in the location of the ascending motion and rain belt shifting obviously northward. Further analyses indicate that the pre-winter ENSO and summer North Atlantic air–sea interaction are two indispensable possible modulation factors for the joint impact of the two subtropical highs on TP precipitation.

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F. Guo
,
S. C. Clemens
,
X. Du
,
X. Liu
,
Y. Liu
,
J. Sun
,
H. Fan
,
T. Wang
, and
Y. Sun

Abstract

Millennial-scale climate change is thought to be synchronous throughout the northern hemisphere and has been demonstrated to be strongly modulated by longer-term glacial-interglacial and orbital scale processes. However, processes that modulate the magnitude of millennial-scale variability (MMV) at the glacial-interglacial timescale remain unclear. We present multi-proxy evidence showing out-of-phase relationships between the MMV of East Asian and North Atlantic climate proxies at the eccentricity band. During most late Pleistocene glacial intervals, the MMV in North Atlantic SST and East Asian Monsoon proxies show a gradual weakening trend from glacial inceptions into glacial maxima, inversely proportional to that of North Atlantic ice rafted detritus record. The inverse glacial-age trends apply to both summer- and winter-monsoon proxies across the loess, speleothem, and marine archives, indicating fundamental linkages between MMV records of the North Atlantic and East Asia. We infer that intensified glacial-age iceberg discharge is accompanied by weakened Atlantic meridional overturning circulation via changes in freshwater input and water-column stability, leading to reduction in North Atlantic SST and wind anomalies, subsequently propagating dampened millennial-scale variability into the mid-latitude East Asian Monsoon region via the westerlies. Our results indicate that the impact of North Atlantic iceberg discharge and the associated variability in water-column stability at the millennial-scale is a primary influence on hydroclimate instability in East Asia.

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Ying Dai
,
Peter Hitchcock
, and
Isla R. Simpson

Abstract

This study evaluates the representation of the composite-mean surface response to Sudden Stratospheric Warmings (SSWs) in 28 CMIP6 models. Most models can reproduce the magnitude of the SLP response over the Arctic, although the simulated Arctic SLP response varies from model to model. Regarding the structure of the SLP response, most models exhibit a basin-symmetric negative NAM-like response with a cyclonic Pacific SLP response, whereas the reanalysis shows a highly basin-asymmetric negative NAO-like response without a robust Pacific center.

We then explore the drivers of these model biases and spread by applying a multiple linear regression. The results show that the polar-cap temperature anomalies at 100 hPa (ΔT 100) modulate both the magnitude of the Arctic SLP response and the cyclonic Pacific SLP response. Apart from ΔT 100, the intensity and latitudinal location of the climatological eddy-driven jet in the troposphere also affect the magnitude of the Arctic SLP response. The compensation of model biases in these two tropospheric metrics and the good model representation of ΔT 100 explains the good agreement between the ensemble mean and the reanalysis on the magnitude of the Arctic SLP response, as indicated by the fact that the ensemble mean lies well within the reanalysis uncertainty range and that the reanalysis mean sits well within the model distribution. The Niño-3.4 SST anomalies and North Pacific SST dipole anomalies together with ΔT 100 modulate the cyclonic Pacific SLP response. In this case, biases in both oceanic drivers work in the same direction and lead to the cyclonic Pacific SLP response in models that is not present in the reanalysis.

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S. Kalluri
,
C. Cao
,
A. Heidinger
,
A. Ignatov
,
J. Key
, and
T. Smith
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