Browse

You are looking at 51 - 60 of 9,957 items for :

  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All
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 (Ri b ) 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 Ri b 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 Ri b 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 Ri b 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 Ri b H parameterizations to drylands. These findings represent an important milestone for the applicability of Ri b 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 (Ri b ) approach. To allow for the Ri b 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 Ri b 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 Ri b 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
Oscar Brousse
,
Charles Simpson
,
Owain Kenway
,
Alberto Martilli
,
E. Scott Krayenhoff
,
Andrea Zonato
, and
Clare Heaviside

Abstract

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models’ evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Significance Statement

Urban climate simulations are subject to spatially heterogeneous biases in urban air temperatures. Common validation methods using official weather stations do not suffice for detecting these biases. Using a dense set of personal weather sensors in London, we detect these biases before proposing an innovative way to correct them with machine learning techniques. We argue that any urban climate impact study should use such a technique if possible and that urban climate scientists should continue investigating paths to improve our methods.

Open access
Jacob Coburn
and
Sara C. Pryor

Abstract

Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes.

Significance Statement

Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.

Restricted access
Zachary J. Suriano
,
Gina R. Henderson
,
Julia Arthur
,
Kricket Harper
, and
Daniel J. Leathers

Abstract

Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin.

Significance Statement

The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their “positive” configuration. Future work should consider the potential for these patterns, and related impacts, to change over time.

Open access
Troy P. Wixson
and
Daniel Cooley

Abstract

Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change.

Restricted access
E. Montoya Duque
,
Y. Huang
,
P. T. May
, and
S. T. Siems

Abstract

Recent voyages of the Australian R/V Investigator across the remote Southern Ocean have provided unprecedented observations of precipitation made with both an Ocean Rainfall and Ice-Phase Precipitation Measurement Network (OceanRAIN) maritime disdrometer and a dual-polarization C-band weather radar (OceanPOL). This present study employs these observations to evaluate the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and the fifth major global reanalysis produced by ECMWF (ERA5) precipitation products. Working at a resolution of 60 min and 0.25° (∼25 km), light rain and drizzle are most frequently observed across the region. The IMERG product overestimated precipitation intensity when evaluated against the OceanRAIN but captured the frequency of occurrence well. Looking at the synoptic/process scale, IMERG was found to be the least accurate (overestimated intensity) under warm-frontal and high-latitude cyclone conditions, where multilayer clouds were commonly present. Under postfrontal conditions, IMERG underestimated the precipitation frequency. In comparison, ERA5’s skill was more consistent across various synoptic conditions, except for high pressure conditions where the precipitation frequency (intensity) was highly overestimated (underestimated). Using the OceanPOL radar, an area-to-area analysis (fractional skill score) finds that ERA5 has greater skill than IMERG. There is little agreement in the phase classification between the OceanRAIN disdrometer, IMERG, and ERA5. The comparisons are complicated by the various assumptions for phase classification in the different datasets.

Significance Statement

Our best quantitative estimates of precipitation over the remote, pristine Southern Ocean (SO) continue to suffer from a high degree of uncertainty, with large differences present among satellite-based and reanalysis products. New instrumentation on the R/V Investigator, specifically a dual-polarization C-band weather radar (OceanPOL) and a maritime disdrometer (OceanRAIN), provide unprecedented high-quality observations of precipitation across the SO that will aid in improving precipitation estimates in this region. We use these observations to evaluate the IMERG and ERA5 precipitation products. We find that, in general, IMERG overestimated precipitation intensity, but captured the frequency of occurrence well. In comparison, ERA5 was found to overestimate the frequency of precipitation. Using the OceanPOL radar, an area-to-area analysis finds that ERA5 has greater skill than IMERG.

Restricted access
Christopher M. Rozoff
,
David S. Nolan
,
George H. Bryan
,
Eric A. Hendricks
, and
Jason C. Knievel

Abstract

Populated urban areas along many coastal regions are vulnerable to landfalling tropical cyclones (TCs). To the detriment of surface parameterizations in mesoscale models, the complexities of turbulence at high TC wind speeds in urban canopies are presently poorly understood. Thus, this study explores the impacts of urban morphology on TC-strength winds and boundary layer turbulence in landfalling TCs. To better quantify how urban structures interact with TC winds, large-eddy simulations (LESs) are conducted with the Cloud Model 1 (CM1). This implementation of CM1 includes immersed boundary conditions (IBCs) to represent buildings and eddy recycling to maintain realistic turbulent flow perturbations. Within the IBCs, an idealized coastal city with varying scales is introduced. TC winds impinge perpendicularly to the urbanized coastline. Numerical experiments show that buildings generate distinct, intricate flow patterns that vary significantly as the city structure is varied. Urban IBCs produce much stronger turbulent kinetic energy than is produced by conventional surface parameterizations. Strong effective eddy viscosity due to resolved eddy mixing is displayed in the wake of buildings within the urban canopy, while deep and enhanced effective eddy viscosity is present downstream. Such effects are not seen in a comparison LES using a simple surface parameterization with high roughness values. Wind tunneling effects in streamwise canyons enhance pedestrian-level winds well beyond what is possible without buildings. In the arena of regional mesoscale modeling, this type of LES framework with IBCs can be used to improve parameters in surface and boundary layer schemes to more accurately represent the drag coefficient and the eddy viscosity in landfalling TC boundary layers.

Significance Statement

This is among the first large-eddy simulation model studies to examine the impacts of tropical cyclone–like winds around explicitly resolved buildings. This work is a step forward in bridging the gap between engineering studies that use computational fluid dynamics models or laboratory experiments for flow through cities and mesoscale model simulations of landfalling tropical cyclones that use surface parameterizations specialized for urban land use.

Restricted access