Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Andrew C. Kren x
  • All content x
Clear All Modify Search
Andrew C. Kren and Richard A. Anthes

Abstract

This study estimates the random error variances and standard deviations (STDs) for four datasets: Global Hawk (GH) dropsondes (DROP), the High-Altitude Monolithic Microwave Integrated Circuit Sounding Radiometer (HAMSR) aboard the GH, the fifth European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), and the Hurricane Weather Research and Forecasting (HWRF) Model, using the three-cornered hat (3CH) method. These estimates are made during the 2016 Sensing Hazards with Operational Unmanned Technology (SHOUT) season in the environment of four tropical cyclones from August to October. For temperature and specific and relative humidity, the ERA5, HWRF, and DROP datasets all have similar magnitudes of errors, with ERA5 having the smallest. The error STDs of temperature and specific humidity are less than 0.8 K and 1.0 g kg−1 over most of the troposphere, while relative humidity error STDs increase from less than 5% near the surface to between 10% and 20% in the upper troposphere. The HAMSR bias-corrected data have larger errors, with estimated error STDs of temperature and specific humidity in the lower troposphere between 1.5 and 2.0 K and between 1.5 and 2.5 g kg−1. HAMSR’s relative humidity error STD increases from approximately 10% in the lower troposphere to 30% in the upper troposphere. The 3CH method error estimates are generally consistent with prior independent estimates of errors and uncertainties for the HAMSR and dropsonde datasets, although they are somewhat larger, likely due to the inclusion of representativeness errors (differences associated with different spatial and temporal scales represented by the data).

Restricted access
Tanya R. Peevey, Jason M. English, Lidia Cucurull, Hongli Wang, and Andrew C. Kren

Abstract

Severe weather events can have a significant impact on local communities because of the loss of life and property. Forecast busts associated with high-impact weather events have been attributed to initial condition errors over data-sparse regions, such as the Pacific Ocean. Numerous flight campaigns have found that targeted observations over these areas can improve forecasts. To better understand the impacts of measurement type and sampling domains on forecast performance, observing system simulation experiments are performed using the National Centers for Environmental Prediction Global Forecast System (GFS) with hybrid 3DEnVar data assimilation and the ECMWF T511 nature run. First, three types of simulated perfect dropsonde observations (temperature, specific humidity, and wind) are assimilated into the GFS over a large idealized sampling domain covering the Pacific Ocean. For the three winter storms studied, forecast error was found to be significantly reduced with all three types of measurements providing the most benefit (%–15% reduction in error). Instances when forecasts are not improved are investigated and concluded to be due to challenging meteorological structures, such as cutoff lows and interactions with atmospheric structures outside the sampling domain. Second, simulated dropsondes are assimilated over sensitive areas and flight tracks established using the ensemble transform sensitivity (ETS) technique. For all three winter storms, forecast error is reduced up to 5%, which is less than that found using an idealized domain. These results suggest that targeted observations over the Pacific Ocean may provide a small improvement to winter storm forecasts over the United States.

Full access
Michael J. Mueller, Andrew C. Kren, Lidia Cucurull, Sean P. F. Casey, Ross N. Hoffman, Robert Atlas, and Tanya R. Peevey

Abstract

A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (V max), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, V max, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and V max errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade V max and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and V max improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest V max degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

Restricted access
Gary A. Wick, Jason P. Dunion, Peter G. Black, John R. Walker, Ryan D. Torn, Andrew C. Kren, Altug Aksoy, Hui Christophersen, Lidia Cucurull, Brittany Dahl, Jason M. English, Kate Friedman, Tanya R. Peevey, Kathryn Sellwood, Jason A. Sippel, Vijay Tallapragada, James Taylor, Hongli Wang, Robbie E. Hood, and Philip Hall

Abstract

The National Oceanic and Atmospheric Administration’s (NOAA) Sensing Hazards with Operational Unmanned Technology (SHOUT) project evaluated the ability of observations from high-altitude unmanned aircraft to improve forecasts of high-impact weather events like tropical cyclones or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. During three field campaigns conducted in 2015 and 2016, the National Aeronautics and Space Administration (NASA) Global Hawk, instrumented with GPS dropwindsondes and remote sensors, flew 15 missions sampling 6 tropical cyclones and 3 winter storms. Missions were designed using novel techniques to target sampling regions where high model forecast uncertainty and a high sensitivity to additional observations existed. Data from the flights were examined in real time by operational forecasters, assimilated in operational weather forecast models, and applied postmission to a broad suite of data impact studies. Results from the analyses spanning different models and assimilation schemes, though limited in number, consistently demonstrate the potential for a positive forecast impact from the observations, both with and without a gap in satellite coverage. The analyses with the then-operational modeling system demonstrated large forecast improvements near 15% for tropical cyclone track at a 72-h lead time when the observations were added to the otherwise complete observing system. While future decisions regarding use of the Global Hawk platform will include budgetary considerations, and more observations are required to enhance statistical significance, the scientific results support the potential merit of the observations. This article provides an overview of the missions flown, observational approach, and highlights from the completed and ongoing data impact studies.

Full access