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Calvin M. Elkins
and
Deanna A. Hence

Abstract

Frequent deep convective thunderstorms and mesoscale convective systems make the Córdoba region, near the Sierras de Córdoba mountain range, one of the most active areas on Earth for hail activity. Analysis of hail observations from trained observers and social media reports cross-referenced with operational radar observations identified the convective characteristics of hail-producing convective systems in central Argentina over a 6-month period divided into early (October–December 2018) and late seasons (January–March 2019). Reflectivity and dual-polarization characteristics from the Córdoba operational radar [Radar Meteorológico Argentina (RMA1)] were used to identify the convective modes of convective cells at time of positive hail indicators. Analysis of ERA5 upper-air and surface data examined convective environments of hail events and identified representative dynamic and thermodynamic environments. A majority of early season hail-producing cells were classified as discrete convection, while discrete and multicell occurrence evened out in the late season. Most hail-producing cells initiated directly adjacent to the Sierras in the late season, while cell initiation and hail production is further spread out in the early season. Dividing convective events into dynamic/thermodynamic regimes based on values of 1000 J kg−1 of CAPE and vertical wind shear of 20 m s−1 results in most early season events reflecting shear-dominant characteristics (low CAPE, high shear) and most late-season events exhibiting CAPE-dominant characteristics (high CAPE, low shear). Strength and placement of low-level temperature and moisture anomalies/advection and upper-level jets largely defined the differences in the dominant regimes.

Significance Statement

This study used regional radar data alongside hail reports from trained observers and social media to better understand the types and timing of storms identified as producing hail, given the lower resolution of satellite studies. Dividing the hail season (October–December; January–March) showed that within hail season, early season storms tended to be singular storms that formed across the region in environments with strong vertical winds and weak instability. Late-season storms were a mix of singular storms and multicellular storm systems focused on the mountains in weak vertical winds and strong instability. These results show differences from satellite studies and identify key representative hail-producing radar features and environmental regimes for this region, which could guide hail risk analysis within the severe-weather season.

Open access
Ricardo C. Muñoz
and
Laurence Armi

Abstract

Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts at any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory, and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon air mass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP–raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.

Open access
Yoonjin Lee
and
Kyle Hilburn

Abstract

Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN’s accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning.

Open access
Sudheer R. Bhimireddy
and
David A. R. Kristovich

Abstract

This study evaluates the methods of identifying the height zi of the top of the convective boundary layer (CBL) during winter (December and January) over the Great Lakes and nearby land areas using observations taken by the University of Wyoming King Air research aircraft during the Lake-Induced Convection Experiment (1997/98) and Ontario Winter Lake-effect Systems (2013/14) field campaigns. Since CBLs facilitate vertical mixing near the surface, the most direct measurement of zi is that above which the vertical velocity turbulent fluctuations are weak or absent. Thus, we use zi from the turbulence method as the “reference value” to which zi from other methods, based on bulk Richardson number (Ri b ), liquid water content, and vertical gradients of potential temperature, relative humidity, and water vapor mixing ratio, are compared. The potential temperature gradient method using a threshold value of 0.015 K m−1 for soundings over land and 0.011 K m−1 for soundings over lake provided the estimates of zi that are most consistent with the turbulence method. The Ri b threshold-based method, commonly used in numerical simulation studies, underestimated zi . Analyzing the methods’ performance on the averaging window z avg we recommend using z avg = 20 or 50 m for zi estimations for lake-effect boundary layers. The present dataset consists of both cloudy and cloud-free boundary layers, some having decoupled boundary layers above the inversion top. Because cases of decoupled boundary layers appear to be formed by nearby synoptic storms, we recommend use of the more general term, elevated mixed layers.

Significance Statement

The depth zi of the convective atmospheric boundary layer (CBL) strongly influences precipitation rates during lake-effect snowstorms (LES). However, various zi approximation methods produce significantly different results. This study utilizes extensive concurrently collected observations by project aircraft during two LES field studies [Lake-Induced Convection Experiment (Lake-ICE) and OWLeS] to assess how zi from common estimation methods compare with “reference” zi derived from turbulent fluctuations, a direct measure of CBL mixing. For soundings taken both over land and lake; with cloudy or cloud-free conditions, potential temperature gradient (PTG) methods provided the best agreement with the reference zi . A method commonly employed in numerical simulations performed relatively poorly. Interestingly, the PTG method worked equally well for “coupled” and elevated decoupled CBLs, commonly associated with nearby cyclones.

Open access
C. Cammalleri
,
N. McCormick
,
J. Spinoni
, and
J. W. Nielsen-Gammon

Abstract

The standardized precipitation index (SPI) is the most commonly used index for detecting and characterizing meteorological droughts, and it is also extensively used as a proxy variable for soil moisture anomalies (SMA) for the purpose of monitoring agricultural drought in absence of long-term soil moisture observations. However, the potential capability of SPI to warn of the time-lagged soil water deficit—following the well-known “drought cascade” effect—is often overlooked in agricultural drought studies. In this research, a time-lagged correlation analysis is used to evaluate the relationship between the SMA dataset, generated as part of the Global Drought Observatory of the European Union’s Copernicus Emergency Management Service, and a set of SPIs derived from the ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts. The possibility to achieve an optimal agreement between SPI and SMA that also preserves the early warning skills of SPI is evaluated. The results suggest that if only the correlation between SPI and SMA is considered, the maximum agreement is usually obtained with a zero lead time (almost 80% of the cases), with SPI-3 representing the best option in about 40% of the grid cells at global scale. By also accounting for the benefits of a positive lead time, short accumulation periods tend to be favored, with SPI-1 being the optimal choice in about one-half of the cases, and 10–20 days of lead time in more than 90% of the grid cells is achieved without any significant reduction in either correlation or skill in drought extreme detection.

Open access
Joana Mendes
,
Nosipho Zwane
,
Brighton Mabasa
,
Henerica Tazvinga
,
Karen Walter
,
Cyril J. Morcrette
, and
Joel Botai

Abstract

We assess site-specific surface shortwave radiation forecasts from two high-resolution configurations of the South African Weather Service numerical weather prediction model, at 4 and 1.5 km. The models exhibit good skill overall in forecasting surface shortwave radiation, with zero median error for all radiation components. This information is relevant to support a growing renewable energy sector in South Africa, particularly for photovoltaics. Further model performance analysis has shown an imbalance between cloud and solar radiation forecasting errors. In addition, cloud overprediction does not necessarily equate to underestimating solar radiation. Overcast cloud regimes are predicted too often with an associated positive mean radiation bias, whereas the relative abundance of partly cloudy regimes is underpredicted by the models with mixed radiation biases. Challenges highlighted by the misrepresentation of partly cloudy regimes in solar radiation error attribution may be used to inform improvements to the numerical core, namely, the cloud and radiation schemes.

Significance Statement

This paper provides the first comprehensive assessment of high-resolution site-specific NWP forecasts of surface shortwave radiation in South Africa, exploring clouds as the main drivers of prediction biases. Error attribution analyses of this kind are close to none for this part of the world. Our study contributes to understanding how cloud and radiation schemes perform over South Africa, representing a step forward in the state of the art. In addition to the scientific interest, the capabilities developed through this work may benefit the second largest economy of the continent. In a country where energy security is of critical relevance, the availability of useful and usable weather information is paramount to support its industry and socioeconomic growth.

Open access
Andra J. Garner
and
Daniel P. Duran

Abstract

Large temperature variations in a temperate climate, particularly in late winter and early spring, can be disruptive for native ecosystems and agricultural crops. As warmer temperatures occur earlier in the year in midlatitude regions as a result of anthropogenic climate change, springtime temperatures may become less consistent, leading to potential damage to species and crops that are vulnerable to the return of historically cooler temperatures, including late-spring frosts, after an initial warm-up. In this work, we quantify shifting patterns in late-winter and springtime temperature variations at eight sites across New Jersey from 1950 to 2019. Many sites located along the coast or in the coastal plain experience increases in the number of times the temperature climbs above 15.5°C (60°F) and then falls below freezing (i.e.,0°C, or 32°F). Sites in southern New Jersey (where much of the state’s agriculture is located) experience the most significant (P < 0.05) increases in large springtime temperature variations. Across all sites, there is a general increase in both the percentage and magnitude of temperature variations that occur as early as February. At 75% of sites, day-to-day variation in daily maximum temperature has increased from the 1950s through 2019; day-to-day variation in daily minimum temperatures has increased over the same time at more than half of sites considered. These amplifications in extreme temperature variations indicate the need for both mitigation and adaptation strategies to protect vulnerable crops and ecosystems in the region during this critical time of the year.

Significance Statement

Human-caused climate change has made it more likely for warmer temperatures to occur earlier in the year, causing many locations to experience late-winter and early-springtime temperatures that are less consistent than they may have been in the past. These variations can be highly problematic for both vital agricultural crops and critical ecosystems. Here, we evaluate how late-winter and early-springtime temperatures have changed throughout New Jersey (home to a variety of agriculture and unique ecosystems) from the mid-twentieth century until 2019. We find critical changes to temperature patterns during late winter and early spring, including larger and more frequent temperature swings (particularly in February) and increased day-to-day variation in high and low temperatures.

Open access
Reinel Sospedra-Alfonso
,
William J. Merryfield
,
Viatsheslav V. Kharin
,
Woo-Sung Lee
,
Hai Lin
,
Gulilat T. Diro
, and
Ryan Muncaster

Abstract

We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and the coupled Global Environmental Multiscale, version 5.1, (GEM5)–NEMO model (GEM5-NEMO), with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2–4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tends to be higher on average during summer and fall and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions.

Open access
Austin P. Hope
,
Israel Lopez-Coto
,
Kris Hajny
,
Jay M. Tomlin
,
Robert Kaeser
,
Brian Stirm
,
Anna Karion
, and
Paul B. Shepson

Abstract

We investigated the ability of three planetary boundary layer (PBL) schemes in the Weather Research and Forecasting (WRF) Model to simulate boundary layer turbulence in the “gray zone” (i.e., resolutions from 100 m to 1 km). The three schemes chosen are the well-established MYNN PBL scheme and the two newest PBL schemes added to WRF: the three-dimensional scale-adaptive turbulent kinetic energy scheme (SMS-3DTKE) and the E–ε parameterization scheme (EEPS). The SMS-3DTKE scheme is designed to be scale aware and avoid the double counting of TKE in simulations within the gray zone. We evaluated their performance using aircraft measurements obtained during three research flights immediately downwind of Manhattan, New York City, New York. The MYNN PBL scheme simulates TKE best, despite not being scale aware and slightly underestimating TKE from observations, whereas the SMS-3DTKE scheme appears to be overly scale aware for the three flights examined, in particular, when combined with the MM5 surface layer scheme. The EEPS scheme significantly underestimates TKE, mostly in the elevated layers of the boundary layer. In addition, we examined the impact of flow over tall buildings on observed TKE and found that only the windiest day showed a significant increase in TKE directly downwind of Manhattan. This impact was not reproduced by any of the model configurations, regardless of the land-use data selected, although the better resolved National Land Cover Database (NLCD) land use led to a slight improvement of the spatial distribution of TKE, implying that more explicit representation of the impact of tall buildings may be needed to fully capture their impact on boundary layer turbulence.

Significance Statement

Because the majority of the world’s population lives in cities, it is important to accurately simulate the atmosphere above and around these cities including the turbulence caused by tall buildings. This turbulence can significantly impact the mixing and dilution of air pollutants and other toxins in highly populated urban environments. The scale of cities often falls into what is known as the “gray zone” for turbulence modeling, which has been analyzed theoretically before but rarely in varied real-world conditions. Our analysis around New York City, New York, suggests that model turbulence schemes can match observations relatively well even at gray zone scales, although newer schemes require refinement, and all schemes tend to underestimate turbulence downwind of tall buildings.

Open access
Luis A. Gil-Alana
and
Marlon J. Castillo

Abstract

In this paper, we perform a fractional integration analysis of the average monthly temperature and precipitation data in 17 departments of Guatemala. Two analyses are performed, the first with the original data and the second with the anomalies based on the period January 1994–December 1999. The results indicate that there is a significant positive time trend in temperatures in the departments of Guatemala (0.0045°C month−1), Quetzaltenango (0.0040°C month−1), Escuintla (0.0034°C month−1), and Huehuetenango (0.0047°C month−1), whereas in the case of precipitation no time trend was observed. An important relevant result is that the departments of El Progreso, Baja Verapaz, and Guatemala occupy the second, third and fourth highest levels of persistence for both temperatures and precipitation, with Sacatepéquez and Quiché displaying the first places for temperature and precipitation, respectively, thus making these five departments the ones that are most vulnerable to climate change since a shock would take a long time to disappear.

Open access