Francois Vandenberghe

Ground-Based Passive Microwave Profiling during Dynamic Weather Conditions

Abstract Short-period (1–5 min) temperature and humidity soundings up to 10-km height are retrieved from ground-based 12-channel microwave radiometer profiler (MWRP) observations. In contrast to radiosondes, the radiometric retrievals provide very high temporal resolution (1 min or less) of thermodynamic profiles, but the vertical resolution, which declines in proportion to the height above ground level, is lower. The high temporal resolution is able to resolve detailed meso-γ-scale thermodynamic and limited microphysical features of various rapidly changing mesoscale and/or hazardous weather phenomena. To illustrate the MWRP capabilities and potential benefits to research and operational activities, the authors present example radiometric retrievals from a variety of dynamic weather phenomena including upslope supercooled fog, snowfall, a complex cold front, a nocturnal bore, and a squall line accompanied by a wake low and other rapid variations in low-level water vapor and temperature.

A Reanalysis System for the Generation of Mesoscale Climatographies

Abstract The use of a mesoscale model–based four-dimensional data assimilation (FDDA) system for generating mesoscale climatographies is demonstrated. This dynamical downscaling method utilizes the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), wherein Newtonian relaxation terms in the prognostic equations continually nudge the model solution toward surface and upper-air observations. When applied to a mesoscale climatography, the system is called Climate-FDDA (CFDDA). Here, the CFDDA system is used for downscaling eastern Mediterranean climatographies for January and July. The downscaling method performance is verified by using independent observations of monthly rainfall, Quick Scatterometer (QuikSCAT) ocean-surface winds, gauge rainfall, and hourly winds from near-coastal towers. The focus is on the CFDDA system’s ability to represent the frequency distributions of atmospheric states in addition to time means. The verification of the month...

Four-Dimensional Variational Assimilation of Water Vapor Differential Absorption Lidar Data: The First Case Study within IHOP_2002

Four-dimensional variational assimilation of water vapor differential absorption lidar (DIAL) data has been applied for investigating their impact on the initial water field for mesoscale weather forecasting. A case that was observed during the International H2O Project (IHOP_2002) has been selected. During 24 May 2002, data from the NASA Lidar Atmospheric Sensing Experiment were available upstream of a convective system that formed later along the dryline and a cold front. Tools were developed for routinely assimilating water vapor DIAL data into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The results demonstrate a large impact on the initial water vapor field. This is due to the high resolution and accuracy of DIAL data making the observation of the high spatial variability of humidity in the region of the dryline and of the cold front possible. The water vapor field is mainly adjusted by a modification of the atmospheric wind field changing the moisture transport. A positive impact of the improved initial fields on the spatial/temporal prediction of convective initiation is visible. The results demonstrate the high value of accurate, vertically resolved mesoscale water vapor observations and advanced data assimilation systems for short-range weather forecasting.

Three-Dimensional Variational Data Assimilation of Ground-Based GPS ZTD and Meteorological Observations during the 14 December 2001 Storm Event over the Western Mediterranean Sea

Abstract The impact of GPS zenith total delay (ZTD) measurements on mesoscale weather forecasts is studied. GPS observations from a permanent European network are assimilated into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) using its three-dimensional variational data assimilation (3DVAR) system. The case study focuses on a snow storm that occurred during the period of 14–15 December 2001 over the western Mediterranean Sea. The experiments show that the most significant improvement in forecast is obtained when GPS ZTD data are assimilated together with local surface meteorological observations into the model within a cycling assimilation framework. In this case, the root-mean-square (rms) differences between forecasted and observed values are reduced by 1.7% in the wind component, 4.1% in the temperature variable, and 17.8% in the specific humidity field. This suggests the deployment of GPS receivers at surface stations to better initialize numerical weather prediction mo...

Identifying PM2.5 and PM0.1 Sources for Epidemiological Studies in California

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.

Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological Studies in California

The University of California-Davis_Primary (UCD_P) chemical transport model was developed and applied to compute the primary airborne particulate matter (PM) trace chemical concentrations from ∼ 900 sources in California through a simulation of atmospheric emissions, transport, dry deposition and wet deposition for a 7-year period (2000-2006) with results saved at daily time resolution. A comprehensive comparison between monthly average model results and available measurements yielded Pearson correlation coefficients (R) ≥ 0.8 at ≥ 5 sites (out of a total of eight) for elemental carbon (EC) and nine trace elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium, manganese, and strontium in the PM2.5 size fraction. Longer averaging time increased the overall R for PM2.5 EC from 0.89 (1 day) to 0.94 (1 month), and increased the number of species with strong correlations at individual sites. Predicted PM0.1 mass and PM0.1 EC exhibited excellent agreement with measurements (R = 0.92 and 0.94, respectively). The additional temporal and spatial information in the UCD_P model predictions produced population exposure estimates for PM2.5 and PM0.1 that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. The UCD_P model has the potential to improve exposure estimates in epidemiology studies of PM trace chemical components and health.

A Study of the Characteristics and Assimilation of Retrieved MODIS Total Precipitable Water Data in Severe Weather Simulations

Abstract This study determined the accuracy and biases associated with retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water (TPW) data, and it investigated the impact of these data on severe weather simulations using the Weather Research and Forecast (WRF) model. Comparisons of MODIS TPW with the global positioning system (GPS) TPW and radiosonde-derived TPW were carried out. The comparison with GPS TPW over the United States showed that the root-mean-square (RMS) differences between these two datasets were about 5.2 and 3.3 mm for infrared (IR) and near-infrared (nIR) TPW, respectively. MODIS IR TPW data were overestimated in a dry atmosphere but underestimated in a moist atmosphere, whereas the nIR values were slightly underestimated in a dry atmosphere but overestimated in a moist atmosphere. Two cases, a severe thunderstorm system (2004) over land and Hurricane Isidore (2002) over ocean, as well as conventional observations and Special Sensor Microwave Imager (SSM/I...

Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data

Abstract Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based ...

Multi-sensor Advection Diffusion nowCast (MADCast) for cloud analysis and short-term prediction

A new approach designed for the analysis and short-term forecasting of clouds, called Multi-sensor Advection-Diffusion nowCast (MADCast), has been implemented within the Weather Research and Forecasting (WRF) model and data assimilation platforms. In this approach, profiles of cloud fractions are retrieved from multiple infrared sensors using the Multivariate Minimum Residual (MMR) scheme. These profiles are then projected to the grid of the numerical weather prediction model, which is used to dynamically transport and diffuse the clouds in three dimensions. NCAR/MMM MADCast system Technical note 2/21 LIST OF CONTENTS 1. INTRODUCTION ..............................................................................................................................4 2. METHODOLOGY ....................................................................................................... 4 2.1 Cloud fraction retrieval ................................................................................................... 5 2.2 3-D Multi sensor approach.............................................................................................. 6 2.3 Forecast and rapid update cycling................................................................................... 7 3. IMPLEMENTATION ............................................................................................... 9 3.1 Processing of all-sky radiances from multiple sensors ................................................... 9 3.2 Modification of the Community Radiative Transfer Model (CRTM) ............................ 9 3.3 Cloud Analysis .............................................................................................................. 10 3.4 WRF forecast ................................................................................................................ 14 4. EXPERIMENT SET-UP......................................................................................... 15 4.1 Software Installation ..................................................................................................... 15 4.2 Input .............................................................................................................................. 15 4.3 Output............................................................................................................................ 15 4.4 Namelist ........................................................................................................................ 16 5. VERIFICATION..................................................................................................... 18 5.1 Cloud fraction................................................................................................................ 19 5.2 Solar irradiance ............................................................................................................. 20 6. CONCLUSIONS..................................................................................................... 20 NCAR/MMM MADCast system Technical Note 3/21 LIST OF FIGURES Figure 1, Flowchart of the MADCast system, which is composed of three main steps: first, it retrieves vertical profile of cloud fraction by pixel from various InfraRed (IR) sensors, then it combines them to perform a single gridded analysis of cloud fraction, and finally a forecast is launched and cycling is allowed. .......................... 5 Figure 2. (a) Size of the interpolation radius as a function of scan angle for IASI. (b) Example of IASI fields of view on the edge of the swath. The 4x4 pixel matrix explains the shape of the curve in (a)......................................................................... 6 Figure 3. (a) Example of raw cloud fraction for IASI. (b) Example of IASI cloud fraction after interpolation. ........................................................................................ 7 Figure 4, Forecast of vertically integrated cloud fraction (in %) from the WRF model at a) t+0h, b) t+1h, c) t+3h and d) t+6h. ........................................................................ 8 Figure 5. Flowchart of the cloud fraction retrieval process, which is performed in an iterative way over the pixels of all the available sensors. First, the MMR scheme computes observed, modelled and overcast radiance by pixel of a sensor. Then, from a first guess defined in cloud fraction, it minimizes a cost function defined in terms of radiance to ultimately interpolate the analysis onto the horizontal grid, thus filing out the gap between pixels. ............................................................................ 11 Figure 6. Flowchart of MADCast post-processing system. The 3-D cloud fraction verification is performed by matching a cloud mask determined from GOES imager satellite data with a cloud mask of WRF determined from the model output. Comparisons of irradiance are done at the location of the SURFRAD/ISIS ground station network......................................................................................................... 18 Figure 7. (a) Example of scores calculated from the regridded observed and modelled cloud masks over a single 6-hour forecast. Maps of contingency table for (b) t+0h, and (c) t+6h. ............................................................................................................. 19 Figure 8. Time series of 10-min averaged Global Horizontal Irradiance (GHI) at Penn State University SURFRAD station (UTC time) for observations (black), model analysis (blue), the 2-h forecast (green), and 5-h forecast (orange). The red line shows the clear sky model simulation. .................................................................... 20

Object-Based Analog Forecasts for Surface Wind Speed

AbstractAnalogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best ref...