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This study explores the structures of the correlations between infrared (IR) brightness temperatures (BTs) from the three water vapor channels of the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite and the atmospheric state. Ensemble-based data assimilation techniques such as the ensemble Kalman filter (EnKF) rely on correlations to propagate innovations of BTs to increments of model state variables. Because the three water vapor channels are sensitive to moisture in different layers of the troposphere, the heights of the strongest correlations between these channels and moisture in clear-sky regions are closely related to the peaks of their respective weighting functions. In cloudy regions, the strongest correlations appear at the cloud tops of deep clouds, and ice hydrometeors generally have stronger correlations with BT than liquid hydrometeors. The magnitudes of the correlations decrease from the peak value in a column with both vertical and horizontal distance. Just how the correlations decrease depend on both the cloud scenes and the cloud structures, as well as the model variables. Horizontal correlations between BTs and moisture, as well as hydrometeors, in fully cloudy regions decrease to almost 0 at about 30 km. The horizontal correlations with atmospheric state variables in clear-sky regions are broader, maintaining non-zero values out to ∼100 km. The results in this study provide information on the proper choice of cut-off radii in horizontal and vertical localization schemes for the assimilation of BTs. They also provide insights on the most efficient and effective use of the different water vapor channels.

We report for the first time on a distributed acoustic sensing (DAS) array using preexisting underground fiber optics beneath the Penn State campus for detecting and characterizing thunder‐induced ground motions. During a half‐hour interval from 03:20–03:50 UTC on 15 April 2019 in State College, PA, we identify 18 thunder‐induced seismic events in the DAS array data. The high‐fidelity DAS data show that the thunder‐induced seismics are very broadband, with their peak frequency ranging from 20 to 130 Hz. We use arrival times of the 18 events to estimate the phase velocity of the near surface, the back azimuth, and location of thunder‐seismic sources that are verified with lightning locations from the National Lightning Detection Network. Furthermore, the dense DAS data enable us to simulate thunder‐seismic wave propagation and full waveform synthetics and further locate the thunder‐seismic source by time‐reversal migration. Interestingly, we found that thunder‐seismic power recorded by DAS is positively correlated with National Lightning Detection Network lightning current power. These findings suggest that fiber‐optic DAS observations may offer a new avenue of studying thunder‐induced seismics, characterizing the near‐surface velocity structure, and probing the thunder‐ground coupling process. Detailed understanding of how weather processes in the atmosphere couple into seismic waves in the solid Earth is extremely important and will require a high‐resolution observation of this coupling in the temporal and spatial scales. This study is the first to demonstrate the use of preexisting telecommunication fiber optics beneath the Penn State campus to characterize thunder‐induced seismic waves in the near surface. The fiber‐optic array is composed of up to tens of kilometers of fiber‐optic cable and can achieve the high resolution at the scale of meters. The high‐fidelity distributed acoustic sensing (DAS) data can provide a detailed characterization of thunder‐induced signals and further track thunder‐induced sources. Moreover, this is the first study to demonstrate the relationship between thunder current energy and DAS recorded ground motion power, suggesting the DAS observations can be useful for characterizing thunder‐ground motions and probing the complex thunder‐ground coupling process. The first observations of thunder‐induced seismic waves are by the distributed acoustic sensing (DAS) array with preexisting telecommunication fiber opticsDense DAS data reveal the characteristics of thunder‐seismic signals and enable seismic full waveform analysis to conductA positive correlation between DAS seismic wave power and lightning current power was found

In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future. Satellite observations are the backbone of modern weather forecast operations, especially for severe weather monitoring and prediction. However, they are also severely underutilized by computer weather models. Many satellite observations impacted by clouds and precipitation are not used in these models, despite their ability to characterize important features of ongoing severe weather events. This study focuses on satellite observations at microwave (MW) frequencies that are impacted by clouds and precipitation. We explore their potential benefits by incorporating them into a computer weather model using data assimilation and examining their impact on severe weather forecasts. We utilize the 10 August 2020 Midwest derecho that produced extensive wind damage as a case study. Because these MW observations contain contributions from precipitating particles within the derecho, assimilating these observations yields better depictions of the derecho structure in the computer model. Consequently, more accurate forecasts of surface gusts are produced. The results of this study suggest a promising avenue for improving severe weather forecasts worldwide in the future, especially for regions that lack the resources and infrastructure to support high‐spatiotemporal‐resolution weather observations. Microwave (MW) all‐sky radiances are impacted by hydrometeors within clouds, complementing cloud‐top dominated infrared all‐sky radiancesAssimilating MW all‐sky radiances leads to improved storm structure in the analyses of the 10 August 2020 Midwest derechoWith improved depiction of the derecho's structure, accurate gust forecasts are obtained 2 hr earlier Microwave (MW) all‐sky radiances are impacted by hydrometeors within clouds, complementing cloud‐top dominated infrared all‐sky radiances Assimilating MW all‐sky radiances leads to improved storm structure in the analyses of the 10 August 2020 Midwest derecho With improved depiction of the derecho's structure, accurate gust forecasts are obtained 2 hr earlier

The structure and evolution of the low‐level jet over the Orinoco River basin is characterized using finer horizontal, vertical, and temporal resolution than possible in previous studies via dynamical downscaling. The investigation relies on a 5‐month‐long simulation (November 2013 to March 2014) performed with the Weather Research and Forecasting model, with initial and boundary conditions provided by the Global Forecast System analysis. Dynamical downscaling is demonstrated to be an effective method to better resolve the horizontal and vertical characteristics of the Orinoco low‐level jet (OLLJ), improving not only the representation of small‐scale jet streaks within the broader region of low‐level wind enhancement but also its diurnal and austral‐summer evolution. The OLLJ is found to be a single stream tube over Colombia and Venezuela with wind speeds greater than 8 m/s and four distinctive cores varying in height under the influence of sloping terrain. The OLLJ has its maximum monthly mean wind speed (13 m/s) and largest spatial extent (2,100 km × 400 km) in January. The maximum mean wind speeds (13–17 m/s) in the diurnal cycle occur in the early morning, whereas wind speeds are a minimum (8–9 m/s) in the late afternoon when a deep, convective boundary layer is present. The intraseasonal variability of the wind speed and potential temperature only presents significant periodicity in the diurnal and semidiurnal scales, with no other meaningful cycles evident during the austral summer. The Orinoco low‐level jet (300 km wide to 2.5 km deep approximately) is present from November to March and can extend over 2,000 km from the Venezuelan coast to central ColombiaIt is weakest (peak wind speeds ~9 m/s) in November and strongest (peak wind speed ~13 m/s) in JanuaryIt is generally most intense (mean wind speeds ~12–15 m/s) in the morning hours (~0700 LST) and least intense (mean wind speeds ~9 m/s) in the afternoon (~1500 LST)

The article “Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales”, written by Yunji ZHANG, Eugene E. CLOTHIAUX, and David J. STENSRUD was originally published electronically on the publisher’s internet portal on 30 of April 2021 without open access. With the author(s)’ decision to opt for Open Choice, the copyright of the article changed on 26 of October 2021 to © The Author(s), 2021 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.

A multimodel short‐range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration program on improved high temperature forecasting during the summer of 2003 is evaluated. Results from this short‐range ensemble system indicate that using the past complete 12 days of forecasts to bias correct today's forecast yields ensemble mean forecasts of 2‐m temperature, 2‐m dewpoint temperature, and 10‐m wind speed that are competitive with or better than those available from any of the model output statistics presently generated operationally in the United States. However, the bias‐corrected ensemble system provides more than just the ensemble mean forecast. Probabilities produced by this system are skilful and reliable, and have been found to be valuable when evaluated in a cost‐loss model. The ensemble appears to provide better guidance for more unlikely events, such as very warm temperatures, that likely have the greatest economic significance. Industries that are sensitive to the weather, such as power companies, transportation and agriculture, may benefit from the probability information provided. Thus, it is possible to develop post‐processing for short‐range ensemble forecasting systems that is competitive with or better than traditional post‐processing techniques, thereby allowing the rapid production of useful and accurate guidance forecasts of many near surface variables. Copyright © 2005 Royal Meteorological Society

A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration program on improved high temperature forecasting during the summer of 2003 is evaluated. Results from this short-range ensemble system indicate that using the past complete 12 days of forecasts to bias correct today's forecast yields ensemble mean forecasts of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speed that are competitive with or better than those available from any of the model output statistics presently generated operationally in the United States. However, the bias-corrected ensemble system provides more than just the ensemble mean forecast. Probabilities produced by this system are skilful and reliable, and have been found to be valuable when evaluated in a cost-loss model. The ensemble appears to provide better guidance for more unlikely events, such as very warm temperatures, that likely have the greatest economic significance. Industries that are sensitive to the weather, such as power companies, transportation and agriculture, may benefit from the probability information provided. Thus, it is possible to develop post-processing for short-range ensemble forecasting systems that is competitive with or better than traditional post-processing techniques, thereby allowing the rapid production of useful and accurate guidance forecasts of many near surface variables.

AbstractThe Mexican monsoon is a significant feature in the climate of the southwestern United States and Mexico during the summer months. Rainfall in northwestern Mexico during the months of July through September accounts for 60 to 80 of the total annual rainfall, while rainfall in Arizona for these same months accounts for over 40 of the total annual rainfall. Deep convection during the monsoon season produces frequent damaging surface winds, flash flooding, and hail and is a difficult forecast problem. Past numerical simulations frequently have been unable to reproduce the widespread, heavy rains over Mexico and the southwestern United States associated with the monsoon.The Pennsylvania State University/National Center for Atmospheric Research mesoscale model is used to simulate 32 successive 24-h periods during the monsoon season. Mean fields produced by the model simulations are compared against observations to validate the ability of the model to reproduce many of the observed features, including the large-scale midtropospheric wind field, southerly low-level winds over the Gulf of California, and the heavy rains over western Mexico. Preliminary analysis of the mean model fields also suggest that the Gulf of California is the dominant moisture source for deep convection over Mexico and the southwestern United States, with upslope flow along the Sierra Madre Occidental advecting low-level gulf moisture into western Mexico during the daytime and southerly flow at the northern end of the gulf advecting gulf moisture into Arizona on most days. These results illustrate the usefulness of four-dimensional data assimilation techniques to create proxy datasets containing realistic mesoscale features that can be used for detailed diagnostic studies.

The ability to discriminate between tornadic and nontornadic thunderstorms is investigated using a mesoscale model. Nine severe weather events are simulated: four events are tornadic supercell thunderstorm outbreaks that occur in conjunction with strong large-scale forcing for upward motion, three events are bow-echo outbreaks that also occur in conjunction with strong large-scale forcing for upward motion, and two are isolated tornadic supercell thunderstorms that occur under much weaker large-scale forcing. Examination of the mesoscale model simulations suggests that it is possible to discriminate between tornadic and nontornadic thunderstorms by using the locations of model-produced convective activity and values of convective available potential energy to highlight regions of likely thunderstorm development, and then using the values of storm-relative environmental helicity (SREH) and bulk Richardson number shear (BRNSHR) to indicate whether or not tornadic supercell thunderstorms are likely. Values of SREH greater than 100 m2s−2indicate a likelihood that any storms that develop will have a midlevel mesocyclone, values of BRNSHR between 40 and 100 m2s−2suggest that low-level mesocyclogenesis is likely, and values of BRNSHR less than 40 m2s−2suggest that the thunderstorms will be dominated by outflow. By combining the storm characteristics suggested by these parameters, it is possible to use mesoscale model output to infer the dominant mode of severe convection.

Visible and infrared satellite images, in combination with detailed landscape information, suggest an appreciable effect of spatial variations in landscape on cumulus cloud formation over relatively flat terrain. These effects are noticeable when forcing from the atmosphere is weak, e.g., when fronts or other disturbances are absent. A case is presented in which clouds are observed to form first over a mesoscale-size area (100 × 300 km) of harvested wheat in Oklahoma, where the ground temperature is warmer than adjoining areas dominated by growing vegetation. In addition, clouds are suppressed over relatively long bands downwind of small manmade lakes and areas characterized by heavy tree cover. The observed variability of cloud relative to landscape type is compared with that simulated with a one-dimensional boundary-layer model. Clouds form earliest over regions characterized by high, sensible heat flux, and are suppressed over regions characterized by high, latent heat flux during relatively dry atmospheric conditions. This observation has significance in gaining understanding of the feedback mechanisms of land modification on climate, as well as understanding relatively short-range weather forecasting.

The current modernization and restructuring of the National Weather Service (NWS) to date has not involved the rehabilitation of the upper-air observing network over North America. The authors discuss the need for a thorough evaluation of the current upper-air observing system that examines both the old (current) rawinsonde network and newer remote sensing networks. The key concern is that the meteorological community has given insufficient attention to the scientific questions underlying the choices of observing systems as related to the needs of operational forecasting. The authors argue that the principal basis for a scientific and objective evaluation of observing strategies is the ability of a particular observing system (or component of a system) to improve numerical weather prediction in the modernized NWS. Given this starting point, a number of possible new (and old) observing systems and observing strategies are discussed that offer the possibility for much improved initialization of mesoscale numerical forecast models. Perhaps the key shift in observation strategy suggested is the establishment of many additional radiosonde and pilot balloon sounding stations that rely on contract observers rather than the NWS workforce. However, a comprehensive and satisfactory upgrading of the upper-air observing system will be possible only after more substantial participation of the scientific community in the planning and implementation processes.

AbstractLow-level jets (LLJs) occur frequently in many parts of the world. These low-level wind speed maxima are important for both the horizontal and vertical fluxes of temperature and moisture and have been found to be associated with the development and evolution of deep convection. Since deep convective activity produces a significant amount of upper-level cloudiness and is responsible for a large fraction of the warm season rainfall in the United States, the relationship between LLJs and deep convection suggests that LLJs are important contributors to regional climate. Results from a number of past studies are reviewed, and the potential for data from the Atmospheric Radiation Measurement program to augment our understanding of low-level jets is discussed.

The Orinoco low‐level jet (OLLJ) is characterized using finer horizontal, vertical, and temporal resolution than possible in previous studies via dynamical downscaling. The investigation relies on a 5‐month‐long simulation (November 2013 to March 2014) performed with the Weather Research and Forecasting (WRF) model, with initial and boundary conditions provided by the Global Forecast System (GFS) analysis. Dynamical downscaling is demonstrated to be an effective method not only to better resolve the horizontal and vertical characteristics of the OLLJ but also to determine the mechanisms leading to its formation. The OLLJ is a single stream tube over Colombia and Venezuela with wind speeds greater than 8 m s−1and four distinctive cores of higher wind speeds varying in height under the influence of sloping terrain. It is an austral summer phenomenon that exhibits its seasonal maximum wind speed and largest spatial extent (2,100 km × 450 km) in January. The maximum diurnal mean wind speeds (13–17 m s−1) at each core location occur at different times during the night and early morning (2300–0900 LST). The momentum balance analysis in a natural coordinate system reveals that the OLLJ results from four phenomena acting together to accelerate the wind: a sea breeze penetration, katabatic flow, three expansion fans, and diurnal variation of turbulent diffusivity. The latter, in contrast to the heavily studied nocturnal low‐level jet in the U.S. Great Plains region, plays a secondary role in OLLJ acceleration. These results imply that LLJs near the equator may originate from processes other than the inertial oscillation and topographic thermal forcing. The Orinoco low‐level jet is the result of four phenomena acting together to accelerate the wind over the valley of the Orinoco River basinOpposite to what happens in higher latitudes, the role of the diurnal variation of turbulent diffusivity in the Orinoco low‐level jet acceleration is secondaryLow‐level jets near the equator may originate from processes other than the inertial oscillation and topographic thermal forcing