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Temple R. Lee
and
Sandip Pal

Abstract

Rawinsonde observations have long been used to estimate the atmospheric boundary layer depth (BLD), which is an important parameter for monitoring air quality, dispersion studies, weather forecast models, and inversion systems for estimating regional surface–atmosphere fluxes of tracers. Although many approaches exist for deriving the BLDs from rawinsonde observations, the bulk Richardson approach has been found to be most appropriate. However, the impact of errors in the measured thermodynamic and kinematic fields on the estimated BLDs remains unexplored. We argue that quantifying BLD error (δBLD) estimates is equally as important as the BLDs themselves. Here we quantified δBLD by applying the bulk Richardson method to 35 years of rawinsonde data obtained from three stations in the United States: Sterling, Virginia; Amarillo, Texas; and Salt Lake City, Utah. Results revealed similar features in terms of their respective errors. A −2°C bias in temperature yielded a mean δBLD ranging from −15 to 200 m. A +2°C bias in temperature yielded a mean δBLD ranging from −214 to +18 m. For a −5% relative humidity bias, the mean δBLD ranged from −302 to +7 m. For a +5% relative humidity bias, the mean δBLD ranged from +2 to +249 m. Differences of ±2 m s−1 in the winds yielded BLD errors of ~±300 m. The δBLD increased as a function of BLD when introducing errors to the thermodynamic fields and decreased as a function of BLD when introducing errors to the kinematic fields. These findings expand upon previous work evaluating rawinsonde-derived δBLD by quantifying δBLD arising from rawinsonde-derived thermodynamic and kinematic measurements. Knowledge of δBLD is critical in, for example, intercomparison studies where rawinsonde-derived BLDs are used as references.

Open access
Nicholas E. Clark
,
Sandip Pal
, and
Temple R. Lee

Abstract

Despite many observational studies on the atmospheric boundary layer (ABL) depth zi variability across various time scales (e.g., diurnal, seasonal, annual, and decadal), zi variability before, during, and after frontal passages over land, or simply zi variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5 years (2014–18) of daytime rawinsonde observations and surface analyses over 18 central and southeastern U.S. sites to report zi variability across frontal boundaries. By providing systematic observations of front-relative contrasts in zi (i.e., zi differences between warm and cold sectors, Δ z i = z i Warm z i Cold ) and boundary layer moisture (i.e., ABL-q) regimes in summer and winter, we propose a new paradigm to study zi changes across cold-frontal boundaries. For most cases, we found deeper zi over the warm sector than the cold sector in both summer and winter, although with significant site-to-site variability in Δzi . Additionally, our results show a positive Δq ABL (i.e., frontal contrasts in ABL-q) in summer and winter, supporting what is typically observed in midlatitude cyclones. We found that a front-relative Δq ABL of 1 g kg−1 often yielded at least a 100-m Δzi across the frontal boundary in both summer and winter. This work provides a synoptic-scale basis for zi variability and establishes a foundation for model verification to examine the impact of airmass exchange associated with advection on zi . This work will advance our understanding of ABL processes in synoptic environments and help unravel sources of front-relative zi variability.

Significance Statement

The atmospheric boundary layer (ABL) is the lowermost part of the atmosphere adjacent to Earth’s surface. The irregular motion of air inside the ABL plays an essential role in relocating air near the surface to the free troposphere. Meteorologists use ABL depth in weather forecast models to determine the atmosphere’s ability to dilute or enrich tracers within the ABL. However, knowledge about the changes in ABL depth during stormy conditions remains incomplete. Here, we investigate how the ABL depth varies before and after cold-frontal passages. We found that ABL depths were much deeper before the cold-frontal passages than after. This knowledge will help us develop new approaches to consider how storms modify the ABL in weather forecast models.

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 (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
Temple R. Lee
,
Sandip Pal
,
Ronald D. Leeper
,
Tim Wilson
,
Howard J. Diamond
,
Tilden P. Meyers
, and
David D. Turner

Abstract

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SW d ) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SW d biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SW d , we found daytime temperature biases of about 3°C (−2.5°C) and daytime SW d biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SW d biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models.

Significance Statement

Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts.

Open access
Erik Crosman
,
Aaron M. Ward
,
Stephen W. Bieda III
,
T. Todd Lindley
,
Mike Gittinger
,
Sandip Pal
, and
Hemanth Vepuri

Abstract

While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.

Open access
Pieter Groenemeijer
,
Christian Barthlott
,
Ulrich Corsmeier
,
Jan Handwerker
,
Martin Kohler
,
Christoph Kottmeier
,
Holger Mahlke
,
Andreas Wieser
,
Andreas Behrendt
,
Sandip Pal
,
Marcus Radlach
,
Volker Wulfmeyer
, and
Jörg Trentmann

Abstract

Measurements of a convective storm cluster in the northern Black Forest in southwest Germany have revealed the development of a warm and dry downdraft under its anvil cloud that had an inhibiting effect on the subsequent development of convection. These measurements were made on 12 July 2006 as part of the field campaign Prediction, Identification and Tracking of Convective Cells (PRINCE) during which a number of new measurement strategies were deployed. These included the collocation of a rotational Raman lidar and a Doppler lidar on the summit of the highest mountain in the region (1164 m MSL) as well as the deployment of teams carrying radiosondes to be released in the vicinity of convective storms. In addition, an aircraft equipped with sensors for meteorological variables and dropsondes was in operation and determined that the downdraft air was approximately 1.5 K warmer, 4 g kg−1 drier, and therefore 3 g m−3 less dense than the air at the same altitude in the storm’s surroundings. The Raman lidar detected undulating aerosol-rich layers in the preconvective environment and a gradual warming trend of the lower troposphere as the nearby storm system evolved. The Doppler lidar both detected a pattern of convergent radial winds under a developing convective updraft and an outflow emerging under the storm’s anvil cloud. The dryness of the downdraft air indicates that it had subsided from higher altitudes. Its low density reveals that its development was not caused by negative thermal buoyancy, but was rather due to the vertical mass flux balance accompanying the storm’s updrafts.

Full access
Kenneth J. Davis
,
Edward V. Browell
,
Sha Feng
,
Thomas Lauvaux
,
Michael D. Obland
,
Sandip Pal
,
Bianca C. Baier
,
David F. Baker
,
Ian T. Baker
,
Zachary R. Barkley
,
Kevin W. Bowman
,
Yu Yan Cui
,
A. Scott Denning
,
Joshua P. DiGangi
,
Jeremy T. Dobler
,
Alan Fried
,
Tobias Gerken
,
Klaus Keller
,
Bing Lin
,
Amin R. Nehrir
,
Caroline P. Normile
,
Christopher W. O’Dell
,
Lesley E. Ott
,
Anke Roiger
,
Andrew E. Schuh
,
Colm Sweeney
,
Yaxing Wei
,
Brad Weir
,
Ming Xue
, and
Christopher A. Williams

Abstract

The Atmospheric Carbon and Transport (ACT)-America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1,140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five 6-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair-weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America dataset and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise subcontinental GHG flux estimates.

Full access