Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

During the second Wind Forecast Improvement Project (WFIP2; October 2015&ndash;March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR &ndash; 3&thinsp;km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST &ndash; 750&thinsp;m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80&thinsp;m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80&thinsp;m wind speed MAE is reduced by 3&thinsp;%&ndash;4&thinsp;% due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5&thinsp;% on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80&thinsp;m wind speed MAE, up to 7&thinsp;%&ndash;8&thinsp;%. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80&thinsp;m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors. </div

Profiling the atmosphere with the airborne radio occultation technique

The GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS) was designed for dense sampling of meteorological targets using the airborne radio occultation (RO) technique. Airborne RO refers to an atmospheric limb sounding technique in which Global Positioning System (GPS) signals are recorded at a receiver onboard an aircraft as the satellites descend beyond the limb of the Earth. The GPS signals, that are unaffected by clouds and precipitation, experience refractive bending as well as a delay in the travel time through the atmosphere. Bending can be used to retrieve information about atmospheric refractivity, which depends on atmospheric moisture and temperature. The new system has the potential for improving numerical weather prediction (NWP) forecasts through assimilation of many high-resolution atmospheric profiles in an area of interest, compared to spaceborne RO, which samples sparsely around the globe. In February 2008, GISMOS was deployed on the National Science Foundation Gulfstream-V aircraft to make atmospheric observations in the Gulf of Mexico coastal region with an objective to test the performance of the profiling system. Recordings from this flight campaign made with the conventional phase lock loop GPS receivers descend from flight level to 5 km altitude. However, below that level strong refractivity gradients, especially those associated with the boundary layer, cause rapid phase accelerations resulting in loss of lock in the receiver. To extend the RO profiles deeper in the atmosphere, the GISMOS system was also equipped with a GPS Recording System (GRS) that records the raw RF signals. Post-processing this dataset in open-loop (OL) tracking mode enables reliable atmospheric profiling at lower altitudes. We present a comprehensive analysis of the performance of the airborne system OL tracking algorithm during a 5 hour flight on 15 February 2008. Excess phase and amplitude profiles for 5 setting and 5 rising occultations were successfully retrieved out of the 19 possible cases. Profiles from rising occultations were retrieved with comparable quality to setting occultations. The only missed occultations were due to missing or poor quality ancillary navigation data from the global tracking network and the aircraft turns. We demonstrate that the OL tracking receiver performs much better than the conventional receivers, consistently tracking as low as 0.5 to 3.4 km. Based on this success rate and the improved global network coverage since 2008 providing navigation data bits, the airborne RO system on a straight flight path today would achieve 3 occultations per hour of flight time. The refractivity profiles retrieved with a geometric optics method show a bias with respect to the European Center for Medium Range Weather Forecasting (ECMWF) analysis profiles. The data were compared with a co-located spaceborne RO profile, and although the airborne data shows a larger bias with respect to ECMWF profiles, there is a correlation of the vertical variations observed with both datasets. The standard deviation of the difference with the ECMWF profile refractivity is less than 1 % in terms of refractivity. The comparison of the retrieved refractivity and a co-located radiosonde station profile shows a bias as well, with a standard deviation of 2.3 % from 5-12 km altitude. Future efforts should be directed at resolving the source of the bias, in which case the data will be quite useful for assimilation. The differences are within the range of the observation errors typically assigned to RO data below 10 km during assimilation. Signal tracking and retrieval in the lower troposphere continues to be a major challenge for spaceborne RO, and has limited the impact of all RO data in NWP in the lower troposphere. Full bandwidth signals from airborne measurements could provide a testbed for improving the quality of future spaceborne RO measurements. The airborne RO technique could potentially be implemented on commercial aircraft to provide dense measurements to improve weather forecasting in busy flight corridors

GPS/INS navigation precision and its effect on airborne radio occultation retrieval

In February 2008 the GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS), developed at Purdue University, was deployed on a Gulfstream V aircraft to make atmospheric observations in the Gulf of Mexico coastal region. The primary objective of the flight campaign was to test the performance of the profiling system in retrieving atmospheric refractivity, which is related directly to temperature and moisture content. 20 dropsonde profiles and 28 extra radiosonde profiles were collected for comparison with measurements from the GISMOS airborne observing system. The largest instrumental errors in the airborne observing system are associated with velocity errors in the navigation system. We investigate the sensitivity of the refractivity retrievals to navigation accuracy. The state-of-the-art Applanix POS/AV 510 Global Positioning System (GPS)/Inertial Navigation System (INS) uses carrier phase differential measurements integrated with an Inertial Measurement Unit (IMU) to produce a highly accurate high-rate navigation solution. The specified accuracy for this navigation system is 5mm/s. In previous simulations of radio occultation retrievals, this level of accuracy has been demonstrated to result in less than 0.5% refractivity error from the surface to about 9 km for an airplane flying at 10 km altitude [Xie et al., 2008]. 0.5% refractivity corresponds to approximately 1K temperature error or 5% humidity error at 2km altitude, which is a minimum requirement for upper air observing systems for meteorology. We use this dataset to confirm that the navigation system is consistent with the specifications, and to demonstrate some practical operational considerations for conducting radio occultation observations. The degradation of the retrieval quality without the IMU data and with a lower accuracy IMU is examined, in order to determine whether less accurate, but still useful data can be retrieved at significantly reduced cost. Without IMU data the east and north components of the velocity errors are estimated to be less than 2 cm/s. It is found that a large bias exists in the retrieved refractivity, which prevents such a system from providing reliable observations. On the other hand, a less accurate IMU, in the absence of large biases, would still provide acceptable accuracy measurements from the surface to 3.5 km altitude. The accuracy of the real-time autonomous GPS/INS navigation solution is investigated to evaluate the potential for on-board real-time processing of radio occultation profiles. The possible use of this airborne observing system for studying the diurnal cycle of moisture is addressed. Observations that accurately characterize this daily cycle can be critical for improving and tuning climate model representations of clouds and convection, especially over oceans. It is found that typical diurnal variations in moisture from the surface to 7.5 km altitude are much larger than the limiting errors in the radio occultation retrievals. Therefore we anticipate that the new airborne RO technique could be used to tackle this problem effectively

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1m) to O(100km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air-sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps