Irina V. Djalalova

Observed and WRF-Simulated Low-Level Winds in a High-Ozone Episode during the Central California Ozone Study

Abstract A case study is carried out for the 29 July–3 August 2000 episode of the Central California Ozone Study (CCOS), a typical summertime high-ozone event in the Central Valley of California. The focus of the study is on the low-level winds that control the transport and dispersion of pollutants in the Central Valley. An analysis of surface and wind profiler observations from the CCOS field experiment indicates a number of important low-level flows in the Central Valley: 1) the incoming low-level marine airflow through the Carquinez Strait into the Sacramento River delta, 2) the diurnal cycle of upslope–downslope flows, 3) the up- and down-valley flow in the Sacramento Valley, 4) the nocturnal low-level jet in the San Joaquin Valley, and 5) the orographically induced mesoscale eddies (the Fresno and Schultz eddies). A numerical simulation using the advanced research version of the Weather Research and Forecasting Model (WRF) reproduces the overall pattern of the observed low-level flows. The physical ...

The Retrieval of Stratus Cloud Droplet Effective Radius with Cloud Radars

In situ samples of cloud droplets by aircraft in Oklahoma in 1997, the Surface Heat Budget of the Arctic Ocean (SHEBA)/First ISCCP Regional Experiment (FIRE)-Arctic Cloud Experiment (ACE) in 1998, and various other locations around the world were used to evaluate a ground-based remote sensing technique for retrieving profiles of cloud droplet effective radius. The technique is based on vertically pointing measurements from highsensitivity millimeter-wavelength radar and produces height-resolved estimates of cloud particle effective radius. Although most meteorological radars lack the sensitivity to detect small cloud droplets, millimeter-wavelength cloud radars provide opportunities for remotely monitoring the properties of nonprecipitating clouds. These highsensitivity radars reveal detailed reflectivity structure of most clouds that are within several kilometers range. In order to turn reflectivity into usable microphysical quantities, relationships between the measured quantities and the desired quantities must be developed. This can be done through theoretical analysis, modeling, or empirical measurements. Then the uncertainty of each procedure must be determined in order to know which ones to use. In this study, two related techniques are examined for the retrieval of the effective radius. One method uses both radar reflectivity and integrated liquid water through the clouds obtained from a microwave radiometer; the second uses the radar reflectivity and an assumption that continental stratus clouds have a concentration of 200 drops per cubic centimeter and marine stratus 100 cm23. Using in situ measurements of marine and continental stratus, the error analysis herein shows that the error in these techniques would be about 15%. In comparing the techniques with in situ aircraft measurements of effective radius, it is found that the radar radiometer retrieval was not quite as good as the technique using radar reflectivity alone. The radar reflectivity alone gave a 13% standard deviation with the in situ comparison, while the radar‐radiometer retrieval gave a 19% standard deviation.

Inferring Fall Attitudes of Pristine Dendritic Crystals from Polarimetric Radar Data

Abstract Single pristine planar ice crystals exhibit some flutter around their preferential horizontal orientation as they fall. This study presents estimates of flutter and analyzes predominant fall attitudes of pristine dendritic crystals observed with a polarization agile Ka-band cloud radar. The observations were made in weakly precipitating winter clouds on slopes of Mt. Washington, New Hampshire. The radar is capable of measuring the linear depolarization ratios in the standard horizontal–vertical polarization basis (HLDR) and the slant 45°–135° polarization basis (SLDR). Both HLDR and SLDR depend on crystal shape. HLDR also exhibits a strong dependence on crystal orientation, while SLDR depends only weakly on orientation. The different sensitivities of SLDR and HLDR to the shape and orientation effects are interpreted to estimate the angular flutter of crystals. A simple analytical expression is derived for the standard deviation of angular flutter as a function of the HLDR to SLDR ratio assuming p...

A wind profiler trajectory tool for air quality transport applications

[1] Horizontal transport is a key factor in air pollution meteorology. In several recent air quality field campaigns, networks of wind profiling Doppler radars have been deployed to help characterize this important phenomenon. This paper describes a Lagrangian particle trajectory tool developed to take advantage of the hourly wind observations provided by these special profiler networks. The tool uses only the observed wind profiles to calculate trajectory positions and does not involve any model physics or parameterizations. An interpolation scheme is used to determine the wind speed and direction at any given location and altitude along the trajectory. Only the horizontal winds measured by the profilers are included because the type of profiling radars used in this study are unable to resolve synoptic-scale vertical motions. The trajectory tool is applied to a case study from the International Consortium for Research on Transport and Transformation air quality experiment conducted during the summer of 2004 (ICARTT-04). During this international field study, air chemistry observations were collected at Chebogue Point, a coastal station in southwestern Nova Scotia, and factor analysis was used to identify time periods when air pollution from the United States arrived at the site. The profiler trajectories are compared to trajectories produced from numerical model initialization fields. The profiler-based trajectories more accurately reflect changes in the synoptic weather pattern that occurred between operational upper air soundings, thereby providing a more accurate depiction of the horizontal transport responsible for air pollution arriving in Nova Scotia.

Snowfall Retrievals Using Millimeter-Wavelength Cloud Radars

It is demonstrated that millimeter-wavelength radars that are designed primarily for cloud studies can be also used effectively for snowfall retrievals. Radar reflectivity–liquid equivalent snowfall rate (Ze–S) relations specifically tuned for Ka- and W-band radar frequencies are applied to measurements taken by vertically pointing ground-based 8-mm cloud radars (MMCR) that are designed for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program and by the nadir-pointing spaceborne 94-GHz CloudSat radar. Comparisons of the MMCR-based snowfall accumulations estimated during experimental events with no significant snowflake riming and controlled gauge measurements indicated an 87% standard deviation between radar and gauge data that is consistent with the uncertainties in the coefficients of the Ze–S relations resulting from variability in snowflake microphysical properties. Comparisons of CloudSat-based snowfall-rate retrievals in heavy snowfall were consistent with estimates from surface S-band precipitation surveillance radars made using algorithms that were specifically designed for use with these radars. A typical difference between the CloudSat and the S-band precipitation radar estimates of snowfall rate for approximately collocated resolution pixels was within a factor of 2, which is of the order of the uncertainty of each estimate. The results of this study suggest that the ground-based and satellite-borne radars operating at Ka and W bands can provide valuable retrieval information on vertical profiles of snowfall, which is an important component of the global water cycle. This information is particularly important in Arctic regions where precipitation information from other sources is scarce.

The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

AbstractThe Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemome...

Evaluation of the Summertime Low-Level Winds Simulated by MM5 in the Central Valley of California

Abstract A season-long set of 5-day simulations between 1200 UTC 1 June and 1200 UTC 30 September 2000 are evaluated using the observations taken during the Central California Ozone Study (CCOS) 2000 experiment. The simulations are carried out using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), which is widely used for air-quality simulations and control planning. The evaluation results strongly indicate that the model-simulated low-level winds in California’s Central Valley are biased in speed and direction: the simulated winds tend to have a stronger northwesterly component than observed. This bias is related to the difference in the observed and simulated large-scale, upper-level flows. The model simulations also show a bias in the height of the daytime atmospheric boundary layer (ABL), particularly in the northern and southern Central Valley. There is evidence to suggest that this bias in the daytime ABL height is not only associated with the large-scale, upper-level b...

Comparison of radar/radiometer retrievals of stratus cloud liquid‐water content profiles with in situ measurements by aircraft

In situ sampling of cloud droplets by aircraft in Oklahoma in 1997 is used to evaluate a ground-based remote sensing technique for retrieving profiles of cloud liquid-water content. The technique uses vertically pointing measurements from a high-sensitivity millimeter-wavelength radar and a collocated dual-frequency microwave radiometer to obtain height-resolved estimates of the liquid content of stratiform clouds. Comparisons with the aircraft measurements are made for 16 overpasses through thin cloud layers within a 1.5-km radius of the remote sensor site. Over a range of liquid-water contents from 0.04 to 0.57 g m -3 the mean difference between the aircraft and the radar/radiometer values was 0.02 g m -3 , and the maximum difference was 0.09 g m -3 . Although the number of comparisons is limited, these results suggest that the ground-based estimates may be sufficiently accurate for many scientific purposes.

A Wind Energy Ramp Tool and Metric for Measuring the Skill of Numerical Weather Prediction Models

AbstractA wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power over short periods of time ) it is more difficult to balance the electric load with power production than during quiescent periods between ramp events. A ramp-specific metric is needed because standard metrics do not give special consideration to ramp events and hence may not provide an appropriate measure of model skill or skill improvement. This RT&M has three components. The first identifies ramp events in the power time series. The second matches in time forecast and observed ramps. The third determines a skill score of the forecast model. This is calculated from a utility operator’s perspective, incorporates phase and duration errors in time as well as power amplitude errors, and recognizes that up and down ramps have different impacts on grid operation. The RT&M integrates skill over a matrix of ramp events of varying amplitudes and durations.

Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach

AbstractParticulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and No...