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Domingo Muñoz-Esparza, Hyeyum Hailey Shin, Teddie L. Keller, Kyoko Ikeda, Robert D. Sharman, Matthias Steiner, Jeff Rawdon, and Gary Pokodner

Abstract

Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this, the Federal Aviation Administration has developed a Precipitous Terrain classification, as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current Precipitous Point Value (PPV) algorithm relies on the terrain characteristics within a local area of 2 NM, and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely turbulence, altimeter setting errors and density altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 2° × 2° region over the Rocky Mountains in Colorado, containing three airports that are surrounded by Precipitous Terrain. Both available turbulence reports and model’s turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter setting errors and density altitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, while density altitude effects are on the contrary predominantly present during the spring and summer months, and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain.

Open access
Natalia Odnoletkova and Tadeusz W. Patzek

Abstract

We have analyzed the long-term temperature trends and extreme temperature events in Saudi Arabia between 1979 and 2019. Our study relies on high-resolution, consistent, and complete ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). We evaluated linear trends in several climate descriptors, including temperature, dewpoint temperature, thermal comfort, and extreme event indices. Previous works on this topic used data from weather station observations over limited time intervals and did not include temperature data for recent years. The years 2010–19 have been the warmest decade ever observed by instrumental temperature monitoring and are the eight warmest years on record. Therefore, the earlier results may be incomplete, and their results may no longer be relevant. Our findings indicate that, over the past four decades, Saudi Arabia has warmed up at a rate that is 50% higher than the rest of the landmass in the Northern Hemisphere. Moreover, moisture content of the air has significantly increased in the region. The increases of temperature and humidity have resulted in the soaring of dewpoint temperature and thermal discomfort across the country. These increases are more substantial during summers, which are already very hot relative to winters. Such changes may be dangerous to people over vast areas of the country. If the current trend persists into the future, human survival in the region will be impossible without continuous access to air conditioning.

Open access
Free access
Baojuan Huai, Michiel R. van den Broeke, Carleen H. Reijmer, and John Cappellen

Abstract

This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients.

Open access
Christoph Böhm, Jan H. Schween, Mark Reyers, Benedikt Maier, Ulrich Löhnert, and Susanne Crewell

Abstract

In many hyperarid ecosystems, such as the Atacama Desert, fog is the most important freshwater source. To study biological and geological processes in such water-limited regions, knowledge about the spatiotemporal distribution and variability of fog presence is necessary. In this study, in situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog dataset that enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog-detection approach is introduced that uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential nonlinear relationships. Relative to a second fog-detection approach based on MODIS cloud-top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 as compared with 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural-network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Open access
Free access
Brittany N. Carson-Marquis, Jianglong Zhang, Peng Xian, Jeffrey S. Reid, and Jared W. Marquis

Abstract

When unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecast meteorological parameters such as surface temperature. To improve near-surface forecasting accuracies during heavy aerosol loadings, we demonstrate the feasibility of incorporating aerosol fields from a global chemical transport model as initial and boundary conditions into a higher-resolution NWP model with aerosol–meteorological coupling. This concept is tested for a major biomass burning smoke event over the northern Great Plains region of the United States that occurred during summer of 2015. Aerosol analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiative fluxes and near-surface temperature are reduced when compared with surface-based observations. This study confirms the ability to decrease biases induced by the aerosol direct effect for regional NWP forecasts during high-impact aerosol episodes through the incorporation of analyses and forecasts from a global aerosol transport model.

Open access
Christopher P. Loughner, Benjamin Fasoli, Ariel F. Stein, and John C. Lin

Abstract

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s Air Resources Laboratory. In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include a new vertical interpolation algorithm for WRF-derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian time scale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show that the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical direction and is in better agreement with observations when compared with the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared with backward-in-time equivalents and were found to be statistically comparable to one another.

Open access
Maike F. Holthuijzen, Brian Beckage, Patrick J. Clemins, Dave Higdon, and Jonathan M. Winter

Abstract

High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

Open access
Cristian Muñoz and David M. Schultz

Abstract

A study of 500-hPa cutoff lows in central Chile during 1979–2017 was conducted to contrast cutoff lows associated with the lowest quartile of daily precipitation amounts (LOW25) with cutoff lows associated with the highest quartile (HIGH25). To understand the differences between low- and high-precipitation cutoff lows, daily precipitation records, radiosonde observations, and reanalyses were used to analyze the three ingredients necessary for deep moist convection (instability, lift, and moisture) at the eastern and equatorial edge of these lows. Instability was generally small, if any, and showed no major differences between LOW25 and HIGH25 events. Synoptic-scale ascent associated with Q-vector convergence also showed little difference between LOW25 and HIGH25 events. In contrast, the moisture distribution around LOW25 and HIGH25 cutoff lows was different, with a moisture plume that was more defined and more intense equatorward of HIGH25 cutoff lows as compared with LOW25 cutoff lows where the moisture plume occurred poleward of the cutoff low. The presence of the moisture plume equatorward of HIGH25 cutoff lows may have contributed to the shorter persistence of HIGH25 events by providing a source for latent-heat release when the moisture plume reached the windward side of the Andes. Indeed, whereas 48% of LOW25 cutoff lows persisted for longer than 72 h, only 25% of HIGH25 cutoff lows did, despite both systems occurring mostly during the rainy season (May–September). The occurrence of an equatorial moisture plume on the eastern and equatorial edge of cutoff lows is fairly common during high-impact precipitation events, and this mechanism could help to explain high-impact precipitation where the occurrence of cutoff lows and moisture plumes is frequent.

Open access