Browse

You are looking at 41 - 50 of 9,953 items for :

  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All
Martin Ridal
,
Jana Sanchez-Arriola
, and
Mats Dahlbom

Abstract

The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create “super observations” (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE–AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity.

Open access
Zachary J. Suriano
,
Charles Loewy
, and
Jamie Uz

Abstract

Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain’s synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25% of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1% and 93.5% by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability.

Significance Statement

By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography.

Open access
Free access
Sisi Chen
,
Lulin Xue
,
Sarah Tessendorf
,
Thomas Chubb
,
Andrew Peace
,
Luis Ackermann
,
Artur Gevorgyan
,
Yi Huang
,
Steven Siems
,
Roy Rasmussen
,
Suzanne Kenyon
, and
Johanna Speirs

Abstract

This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.

Significance Statement

Winter orographic cloud seeding has been conducted for decades over the Snowy Mountains of Australia for securing water resources. However, this study is the first to perform cloud-seeding simulation for a robust, event-based seeding impact evaluation. A state-of-the-art cloud-seeding model (WRF-WxMod) was used to simulate the cloud seeding and quantified its impact on the region. The Southern Hemisphere, due to low aerosol emissions and highly pristine cloud conditions, has distinctly different cloud microphysical characteristics than the Northern Hemisphere, where WRF-WxMod has been successfully applied in a few regions over the United States. The results showed that WRF-WxMod could accurately capture the clouds and precipitation in both the natural and seeded conditions.

Restricted access
Jingzhuo Wang
,
Jing Chen
,
Hanbin Zhang
,
Ruoyun Ma
, and
Fajing Chen

Abstract

To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.

Restricted access
Francesco Battaglioli
,
Pieter Groenemeijer
,
Tomáš Púčik
,
Mateusz Taszarek
,
Uwe Ulbrich
, and
Henning Rust

Abstract

We have developed additive logistic models for the occurrence of lightning, large hail (≥2 cm), and very large hail (≥5 cm) to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients-based approach. The resulting hail models outperform the significant hail parameter, and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the −10°C isotherm was found to be a more universally skillful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modeled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian plains. In the southern plains, a period of enhanced storm activity occurred in the 1980s and 1990s.

Open access
Temple R. Lee
,
Sandip Pal
,
Praveena Krishnan
,
Brian Hirth
,
Mark Heuer
,
Tilden P. Meyers
,
Rick D. Saylor
, and
John Schroeder

Abstract

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations’ applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity (u*), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u* and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations’ performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u*) and turbulence (i.e., TKE) quantities, although caution is needed when applying the Rib H parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth’s surface covered by drylands.

Significance Statement

Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST’s limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models.

Open access
Felix Erdmann
and
Dieter R. Poelman

Abstract

Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ-LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ-level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter.

Open access
Xin Xu
,
Xuelong Chen
,
Dianbin Cao
,
Yajing Liu
,
Luhan Li
, and
Yaoming Ma

Abstract

The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm ) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw ) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites.

Significance Statement

For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.

Restricted access
Ronald D. Leeper
,
Michael A. Palecki
,
Matthew Watts
, and
Howard Diamond

Abstract

Remotely sensed soil moisture observations provide an opportunity to monitor hydrological conditions from droughts to floods. The European Space Agency’s (ESA) Climate Change Initiative has released both Combined and Passive datasets, which include multiple satellites’ measurements of soil moisture conditions since the 1980s. In this study, both volumetric soil moisture and soil moisture standardized anomalies from the U.S. Climate Reference Network (USCRN) were compared with ESA’s Combined and Passive datasets. Results from this study indicate the importance of using standardized anomalies over volumetric soil moisture conditions as satellite datasets were unable to capture the frequency of conditions observed at the extreme ends of the volumetric distribution. Overall, the Combined dataset had slightly lower measures of soil moisture anomaly errors for all regions; although these differences were not statistically significant. Both satellite datasets were able to detect the evolution from worsening to amelioration of the 2012 drought across the central United States and 2019 flood over the upper Missouri River basin. While the ESA datasets were not able to detect the magnitude of the extremes, the ESA standardized datasets were able to detect the interannual variability of extreme wet and dry day counts for most climate regions. These results suggest that remotely sensed standardized soil moisture can be included in hydrological monitoring systems and combined with in situ measures to detect the magnitude of extreme conditions.

Significance Statement

This study examines how well soil moisture extremes, wet or dry, can be detected from space using one of the lengthiest remotely sensed soil moisture datasets. Comparisons with high-quality station data from the U.S. Climate Reference Network revealed the satellite datasets could capture the frequency of extreme conditions important for climate monitoring, but often missed the absolute magnitudes of the extremes. Future research should focus on how to combine satellite and station data to improve the detection of extreme values important for monitoring.

Restricted access