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Laurence Coursol
,
Sylvain Heilliette
, and
Pierre Gauthier

Abstract

With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices, the results in terms of analysis error are similar.

Significance Statement

Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping.

Open access
Matthew D. LaPlante
,
Luthiene Alves Dalanhese
,
Liping Deng
, and
Shih-Yu Simon Wang

Abstract

Annual wheat yields have steadily risen over the past century, but harvests remain highly variable and dependent on myriad weather conditions during a long growing season. In Kansas, for example, the 2014 crop year brought the lowest average yield in decades at 28 bushels per acre, while in 2016 farmers in the Wheat State, as Kansas is often called, enjoyed an historic high of 57 bushels per acre. It is broadly known that remote forces like the El Niño-Southern Oscillation contribute to meteorological outcomes across North America, including in the wheat growing regions of the U.S. Midwest, but the differential imprints of ENSO phases and flavors have not been well explored as leading indicators for harvest outcomes in highly specific agricultural regions, such as the more than 7 million acres upon which wheat is grown in Kansas. Here, we demonstrate a strong, steady, and long-term association between a simple “wheat yield index” and sea surface temperature anomalies, more than a year earlier, in the East Pacific, potentially offering insights into forthcoming harvest yields several seasons before planting commences.

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Benjamin Le Roy
,
Aude Lemonsu
,
Robert Schoetter
, and
Tiago Machado

Abstract

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical-dynamical downscaling approach (SDD) is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970-2099, while maintaining the city’s land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum(maximum) temperatures (TN/TX) that are too high(low). After correction and downscaling, the urban simulations inherit some of these biases, but give satisfactory results for summer urban heat islands (UHI), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 K and 4.8 K for TN and TX for RCP8.5. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 K to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content, and are partially offset by increased ground heat storage in the city.

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Zhixing Xie
,
Katja Friedrich
,
Sarah A. Tessendorf
,
Lulin Xue
,
Sisi Chen
,
Theodore Whittock
,
Bart Geerts
, and
Kyoko Ikeda

Abstract

Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the intermountain west of the U.S. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multi-scale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder-feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding.

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Jacob T. Carlin
,
Elizabeth N. Smith
, and
Katherine Giannakopoulos

Abstract

Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (ZDR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle.

Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large RMSDs coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data revealed signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal was found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data.

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Free access
Tim Cowan
,
Matthew C. Wheeler
,
David H. Cobon
,
John B. Gaughan
,
Andrew G. Marshall
,
Wendy Sharples
,
Jillian McCulloch
, and
Chelsea Jarvis

Abstract

Exposure to weather extremes, such as heatwaves, can cause discomfort, harm, or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure risk to chill for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multiyear climatology of three cattle thermal heat stress indices across Australia in the warm season months (October–March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the seven historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, and high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events and, importantly, calls for further research on standardizing the risk classifications of these thermal indices for cattle in Australia’s variable climate.

Significance Statement

Cattle across Australia’s northern tropical and semiarid regions often experience extreme hot and humid conditions in the summer months, which increases the risk of heat stress. This is the first study of its kind to evaluate observations of cattle heat stress across Australia using indices that describe the combined effects of solar exposure, wind speed, relative humidity, and surface temperatures. These cattle heat stress indices can be used to evaluate historical cattle mortality events in feedlots and in grazed pastures. This study lays the groundwork for the development of Australian-wide cattle heat stress forecast products on the 7-day to multiweek time scales.

Open access
Saranya Sasidharan
,
V. K. Anandan
, and
Sourin Mukhopadhyay

Abstract

This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity Z DR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and Z DR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation.

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Neil F. Laird
,
Caitlin C. Crossett
,
Catherine J. Britt
,
Nicholas D. Metz
,
Kelly Carmer
, and
Braedyn D. McBroom

Abstract

An investigation of lake effect (LE) and the associated synoptic environment is presented for days when all five lakes in the Great Lakes (GL) region had LE bands [five-lake days (5LDs)]. The study utilized an expanded database of observed LE clouds over the GL during 25 cold seasons (October–March) from 1997/98 to 2021/22. LE bands occurred on 2870 days (64% of all cold-season days). Nearly a third of all LE bands occurred during 5LDs, although 5LDs consisted of just 17.1% of LE days. A majority of 5LDs (56.5%) had lake-to-lake (L2L) bands, and these days comprised 43.5% of all L2L occurrences. 5LDs occurred with a mean of 26.1 (SD = 6.2) days per cold season until 2008/09 and then decreased to a mean of 13.8 (SD = 5.5) days during subsequent cold seasons. January and February had the largest number of consecutive LE days in the GL with a mean of 5.7 and 5.4 days, respectively. As the number of consecutive LE days increases, both the number of 5LDs and the occurrence of consecutive 5LD increase. This translates to an increased potential of heavy snowfall impacts in multiple, localized areas of the GL for extended time periods. The mean composite synoptic pattern of 5LDs exhibited characteristics consistent with lake-aggregate disturbances and showed similarity to synoptic patterns favorable for LE over one or two of the GL found by previous studies. The results demonstrate that several additional areas of the GL are often experiencing LE bands when a localized area has active LE bands occurring.

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Nicolas G. Alonso-De-Linaje
,
Andrea N. Hahmann
,
Ioanna Karagali
,
Krystallia Dimitriadou
, and
Merete Badger

Abstract

The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind Lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the one-year-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples.

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