Abstract

Many large grass fires occurred in north Texas and southern Oklahoma on 9 April 2009, destroying hundreds of homes and businesses and burning thousands of acres of grasslands, producing large smoke and debris plumes that were visible from various remote sensing platforms. At the same time, strong westerly winds were transporting large amounts of dust into the region, mixing with the smoke and debris already being generated. This research uses surface- and satellite-based remote sensing observations of this event to assess the locations of fires and the spatial distribution of smoke and dust aerosols. The authors present a unique perspective by analyzing radar observations of fire debris in conjunction with the satellite analysis of submicrometer smoke aerosol particles. Satellite data clearly show the location of the individual fires and the downwind smoke plumes as well as the large dust storm present over the region. In particular, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness at 0.55 μm within the dust plume was around 0.5, and it increased to greater than 1.0 when combined with smoke. Using the difference in 11- versus 12-μm brightness temperature data combined with surface observations, the large extent of the dust plume was evident through much of north-central Texas, where visibilities were low and the 11–12-μm brightness temperature difference was negative. Conversely, smoke plumes were characterized by higher reflectance at 0.6 μm (visible wavelength). Cross sections of radar data through the several smoke and debris plumes indicated the burnt debris reached up to 5 km into the atmosphere. Plume height output from modified severe storm algorithms produced similar values. Since smoke aerosols are smaller and lighter when compared with the debris, they were likely being transported even higher into the atmosphere. These results show that the combination of satellite and radar data offers a unique perspective on observing the characteristics and evolution of smoke and debris plume emanating from grass fire events.

Abstract

The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SST-related predictors. The addition of the microwave predictors produces a 2%–8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

Abstract

Three hurricanes, Claudette (2003), Isabel (2003), and Dora (1999), were selected to examine the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI) forecast accuracy for three particular storm types. This research was conducted using model analyses and tropical cyclone best-track data, with forecasts generated from a dependent sample. The model analyses and best-track data are assumed to be a “perfect” representation of the actual event (e.g., perfect prog assumption). Analysis of intensity change forecasts indicated that SHIPS-MI performed best, compared to operational SHIPS output, for tropical cyclones that were intensifying from tropical storm to hurricane intensity. Passive microwave imagery, which is sensitive to the intensity and coverage of precipitation, improved intensity forecasts during these periods with a positive intensity change contribution resulting from above normal inner-core precipitation. Forecast improvement was greatest for 12–36-h forecasts, where the microwave contribution to SHIPS-MI was greatest. Once a storm reached an intensity close to its maximum potential intensity, as in the case of Isabel and Dora, both SHIPS and SHIPS-MI incorrectly forecast substantial weakening despite the positive contribution from microwave data. At least in Dora’s case, SHIPS-MI forecasts were slightly stronger than those of SHIPS. Other important contributions to SHIPS-MI forecasts were examined to determine their importance relative to the microwave inputs. Inputs related to sea surface temperature (SST) and persistence–climatology proved to be very important to intensity change forecasts, as expected. These predictors were the primary factor leading to the persistent weakening forecasts made by both models for Isabel and Dora. For Atlantic storms (Claudette and Isabel), the contribution from shear also proved important at characterizing the conduciveness of the environment toward intensification. However, the shear contribution was often small as a result of multiple offsetting shear-related predictors. Finally, it was observed that atmospheric parameters not included in SHIPS, such as eddy momentum flux, could substantially affect the intensity, leading to large forecast errors. This was especially true for the Claudette intensity change forecasts throughout its life cycle.

Abstract

One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

Abstract

The sensitivity of assimilating satellite retrievals of cloud water path (CWP) to the microphysics scheme used by a convection-allowing numerical model is explored. All experiments use the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW), with observations assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter and a 40-member ensemble. Three-dimensional idealized supercell simulations are generated from a deterministic WRF nature run started from a homogeneous set of initial conditions. Four cloud microphysics schemes are tested: Lin–Farley–Orville (LFO), Thompson (THOMP), Morrison double-moment (MOR), and Milbrandt–Yau (MY).

For the idealized experiments, assimilating CWP generates a mature supercell after approximately 1 h for all microphysics schemes. Vertical profiles of ensemble covariances show large differences in the relationship between CWP and various hydrometeor mixing ratios. While the differences in overall CWP are small, the experiments generate very different reflectivity analyses of the simulated storm, with MOR and MY underestimating reflectivity by a large margin. Vertical profiles of hydrometeor mixing ratios from each experiment are generally consistent with scheme design, such that the Thompson scheme characterizes the storm top as mostly snow whereas the Milbrandt–Yau scheme characterizes the storm top as mostly ice. The impacts of these differences on 30-min forecasts show that MOR and MY are unable to maintain convection within the model while THOMP and LFO perform somewhat better, though all fail to capture the divergent movement of the storm split in the nature run.

Abstract

Dual-polarimetric microwave wavelength radar observations of an apartment fire in Huntsville, Alabama, on 3 March 2008 are examined to determine the radar-observable properties of ash and fire debris lofted into the atmosphere. Dual-polarimetric observations are collected at close range (<20 km) by the 5-cm (C band) Advanced Radar for Meteorological and Operational Research (ARMOR) radar operated by the University of Alabama in Huntsville. Precipitation radars, such as ARMOR, are not sensitive to aerosol-sized (D < 10 μm) smoke particles, but they are sensitive to the larger ash and burnt debris embedded within the smoke plume. The authors also assess if turbulent eddies caused by the heat of the fire cause Bragg scattering to occur at the 5-cm wavelength.

In this example, the mean reflectivity within the debris plume from the 1.3° elevation scan was 9.0 dBZ, with a few values exceeding 20 dBZ. The plume is present more than 20 km downstream of the fire, with debris lofted at least 1 km above ground level into the atmosphere. Velocities up to 20 m s⁻¹ are present within the plume, indicating that the travel time for the debris from its source to the maximum range of detection is less than 20 min. Dual-polarization observations show that backscattered radiation is dominated by nonspherical, large, oblate targets as indicated by nonzero differential reflectivity values (mean = 1.7 dB) and low correlation coefficients (0.49). Boundary layer convective rolls are also observed that have very low reflectivity values (−6.0 dBZ); however, differential reflectivity is much larger (3.2 dB). This is likely the result of noise, because ARMOR differential reflectivity is not reliable for reflectivity values <0 dBZ. Also, copolar correlation is even lower compared to the debris plume (0.42). The remainder of the data mainly consists of atmospheric and ground-clutter noise. The large differential phase values coupled with positive differential reflectivity strongly indicate that the source of much of the return from the debris plume is particle scattering. However, given the significant degree of noise present, a substantial contribution from Bragg scattering cannot be entirely ruled out.

Abstract

Nearly 100 000 vortex detections produced by the Mesocyclone Detection Algorithm (MDA) are analyzed to gain insight into the effectiveness of the detection algorithm in identifying various types of tornado-producing events. Radar and algorithm limitations prevent raw vortex detections from being very useful without further discrimination. Filtering techniques are developed to remove spurious vortex detections and discriminate between vortices that are and are not related to mesocyclones.

To investigate whether various vortex detections (and their attributes) are associated with severe weather phenomena, they are compared with available tornado reports to determine if detections with certain types of attributes can be associated with tornadic events. Tornado reports are used since the ground truth tornado set is more reliable than other databases of severe weather phenomena. Basic skill scores and more advanced principal component methods are used to quantify the correlation between vortex detection attributes and tornadoes.

The results of this analysis reveal that only a very small percentage (<5%) of vortex detections, using the most basic definition, are associated with the occurrence of a tornado. Percentages increase to approximately 10% as the criteria for defining a vortex detection as a mesocyclone detection become more strict; however, many tornadic events are only associated with weaker detections and are “missed” when the detection threshold is increased. Several velocity-derived detection attributes are shown to have weak to moderate predictive ability when determining whether a detection is (or is not) tornadic.

Abstract

The evolution of Hurricane Erin (2001) is presented from the perspective of its environmental and inner-core conditions, particularly as they are characterized in the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI). Erin can be described as having two very distinct periods. The first, which occurred between 1 and 6 September 2001, was characterized by a struggling tropical storm failing to intensify as the result of unfavorable environmental and inner-core conditions. The surrounding environment during this period was dominated by moderate shear and mid- to upper-level dry air, both caused in some part by the presence of a Saharan air layer (SAL). Further intensification was inhibited by the lack of sustained deep convection and latent heating near the low-level center. The authors attribute this in part to negative effects from the SAL. The thermodynamic conditions associated with the SAL were not well sampled by the SHIPS parameters, resulting in substantial overforecasting by both SHIPS and SHIPS-MI. Instead, the hostile conditions surrounding Erin caused its dissipation on 6 September. The second period began on 7 September when Erin re-formed north of the original center. Erin began to pull away from the SAL and moved over 29°C sea surface temperatures, beginning a rapid intensification phase and reaching 105 kt by 1800 UTC 9 September. SHIPS-MI forecasts called for substantial intensification as in the previous period, but this time the model underestimated the rate of intensification. The addition of inner-core characteristics from passive microwave data improved the skill somewhat compared to SHIPS, but still left much room for improvement. For this period, it appears that the increasingly favorable atmospheric conditions caused by Erin moving away from the SAL were not well sampled by SHIPS or SHIPS-MI. As a result, the intensity change forecasts were not able to take into account the more favorable environment.

Abstract

Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH).

Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m⁻² compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.