Search Results

You are looking at 1 - 10 of 12 items for

  • Author or Editor: Christopher Grassotti x
  • Refine by Access: All Content x
Clear All Modify Search
Louis Garand and Christopher Grassotti

Abstract

This study explores the feasibility of performing an objective analysis of instantaneous rain rate combining satellite estimates (and eventually other types of observations) with those from a numerical prediction model using the method of statistical interpolation. Results demonstrate that the quality of the short-term precipitation forecasts serving as background field has reached a level that makes such an objective analysis possible.

The two main requirements to obtain an accurate analysis from available information are a realistic estimate of background field and observation errors and knowledge of the horizontal correlation of these errors with distance. The importance of specifying the errors for joint model-observation situations is emphasized; it is especially important in situations where model and observations are in conflict. These aspects of the problem are studied using collocated 6-h forecast with satellite estimates derived from visible and infrared imagery, and ground-truth rainfall data available over Japanese territory from the Global Precipitation Climatology Project. Over 90 000 truth-model-satellite collocations are available at the common scale of 130 km × 130 km. An alternative means of establishing the model error correlation with distance and azimuth direction from 6- and 18-h forecast differences valid at the same time yield results that are similar to those derived from collocations with truth rainfall over large domains, but not locally, this result suggests a means of relaxing the assumption of homogeneity and isotropy of model errors. The sensitivity of the rain rate analysis to different specifications of the satellite to model error ratios is shown with an example.

Full access
Christopher Grassotti and Louis Garand

Abstract

Using Global Precipitation Climatology Project data gathered during June, July, and August 1989 over Japan, rainfall estimates are examined from both geostationary satellite imagery using a multifeature classification approach, and from short-term weather prediction model fields. Additionally, the utility of combining model forecast information within such a classifier to improve the final estimate is investigated. During both months satellite estimates are superior to model forecasts in detecting heavy rain events associated with extremely cold cloud tops, and in identifying cloud-free regions. Model estimates are superior to satellite retrievals in terms of dynamic range and regional bias. Addition of visible data to an infrared-only scheme improved monthly rainfall estimates during June, and hourly estimates during both months. In June it is shown that a combined satellite-model method clearly yields improved retrievals of rainfall relative to those obtained by using either satellite data or model forecasts alone at both monthly and hourly time scales. However, in July and August, satellite retrievals largely underestimated monthly rainfall, and the model produced poor hourly forecasts. Generally, a good model forecast of rainfall can enhance the satellite estimate, while a poor forecast will degrade it. If results obtained during June are found to be valid for other regions of the globe, such a method could be used to develop rainfall climatologies. It could also be used in a real-time operational numerical weather prediction environment since it is computationally rapid, with only geostationary satellite observations and model-predicted fields needed to derive the estimates.

Full access
Ross N. Hoffman and Christopher Grassotti

Abstract

A variational analysis method to detect and correct displacement and amplification errors in short-range forecasts of a data assimilation system is developed and tested. Collectively these errors are termed distortion errors. The method uses a variational approach to solve a nonlinear least squares estimation problem with side constraints to determine the distortion that alters an a priori background field to best fit the available observations. In this study, the data are Special Sensing Microwave/Imager (SSM/I) retrievals of integrated water vapor and the a priori background fields are analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). In practice the background fields would be operational 6-h forecasts.

The necessary algorithms and methodologies were developed, implemented, and tested on a sufficient number of cases to demonstrate the utility of the method. Cases were selected that have noticeable features in the SSM/I vertically integrated water vapor fields. In all cases studied, the SSM/I data, together with the distortion representation of error, produces significant changes to the ECMWF analyses, reducing the variance of the difference between the analysis and SSM/I data by 45%–86%. Further work is suggested to examine impacts on objective analyses and subsequent numerical forecasts.

Full access
Christopher Grassotti, Haig Iskenderian, and Ross N. Hoffman

Abstract

Discrepancies between estimates of rainfall from ground-based radar and satellite observing systems can be attributed to either calibration differences or to geolocation and sampling differences. These latter include differences due to radar or satellite misregistration, differences in observation times, or variations in instrument and retrieval algorithm sensitivities. A new methodology has been developed and tested for integrating radar- and satellite-based estimates of precipitation using a feature calibration and alignment (FCA) technique. The parameters describing the calibration and alignment are found using a variational approach, and are composed of displacement and amplitude adjustments to the satellite rainfall retrievals, which minimize the differences with respect to the radar data and satisfy additional smoothness and magnitude constraints. In this approach the amplitude component represents a calibration of the satellite estimate to the radar, whereas the displacement components correct temporal and/or geolocation differences between the radar and satellite data.

The method has been tested on a number of cases of the NASA WetNet PIP-2 dataset. These data consist of coincident estimates of rainfall by ground-based radar and the DMSP SSM/I. Sensitivity tests were conducted to tune the parameters of the algorithm. Results indicate the effectiveness of the technique in minimizing the discrepancies between radar and satellite observations of rainfall for a variety of rainfall events ranging from midlatitude frontal precipitation to heavy convection associated with a tropical cyclone (Hurricane Andrew). A remaining issue to be resolved is the incorporation of knowledge about location dependencies in the errors of the radar and microwave estimates.

Once the satellite data have been adjusted to match the radar observations, the two independent estimates (radar and adjusted SSM/I rain rates) may be blended to improve the overall depiction of the rainfall event in a single analysis. The FCA technique also has potential applications in 1) the development of satellite rainfall retrieval algorithms that may be tuned to radar rain rates and 2) error assessment of rainfall predictions using radar or satellite rain rates as verification.

Full access
Louis Garand, Christopher Grassotti, Jacques Hallé, and Gerald L. Klein

Radiosonde humidity distributions over the United States, Canada, and Europe are discussed. Striking dry-end and wet-end differences are caused by the lack of international standards in the transformation of relative humidity observations to dewpoint depression and in differing ways of calibrating data taken from the same type of instrument. Differences in sondes used in these regions are also discussed and an example of a dual ascent is shown. Some implications for remote sensing and weather prediction are highlighted.

Full access
Ross N. Hoffman, Christopher Grassotti, and S. Mark Leidner

Abstract

To examine the accuracy of the SeaWinds scatterometer wind data and rain flags, and how this accuracy depends on ground-based radar-estimated rain rate, SeaWinds data, WSI NEXRAD precipitation rates, and selected Eta analysis variables are collocated. [SeaWinds is the NASA scatterometer on the QuikSCAT and Advanced Earth Observing Satellite (ADEOS)-2 satellites, WSI NEXRAD precipitation data are from a Weather Services International Corporation product based on the U.S. Next Generation Radar (NEXRAD) network of Weather Surveillance Radar-1988 Doppler (WSR-88D) installations, and Eta is the NCEP operational mesoscale model.] Only data close to the east coast of the United States are collected, where both the WSI NEXRAD data and the Eta analyses are accurate.

For the subset of data for which WSI NEXRAD detects no rain, within the optimal part of the swath, and for Eta analysis wind speeds between 3 and 20 m s−1, the rms differences between SeaWinds and Eta analysis wind speed and direction are 1.73 m s−1 and 21°, respectively. These rms differences increase significantly whenever WSI NEXRAD detects rain, even light rain. The SeaWinds rain indices are strongly correlated with the WSI NEXRAD precipitation rates. While for high rain rates most winds are correctly flagged, many cases of light rain are not detected.

Full access
Godelieve Deblonde, Louis Garand, Pierre Gauthier, and Christopher Grassotti

Abstract

Total precipitable water (TPW) retrieved from Special Sensor Microwave/lmager (SSM/I) brightness temperatures and specific humidity retrieved from Geostationary Operational Environmental Satellite (GOES) radiances are assimilated using a one-dimensional (ID) variational analysis technique. The study is divided into two parts. First, collocations with radiosondes are performed to arm the quality of the satellite water vapor retrievals. Collocations are also performed with 6-h forecast Acids. Second, SSM/I TPW and GOES specific humidity are assimilated using a ID variational analysis technique that minimizes the error variance of the analyzed field.

A global collocation study over the oceans for SSM/I TPW retrievals and 6-h forecasts of TPW shows that the rmse (with respect to radiosondes) are, respectively, 4.7 and 5.0 kg m−2. A separate collocation study over both the oceans and land for GOES retrieved TPW and 6-h forecasts of TPW yields rmse of 4.6 and 4.4 kg m−2, respectively, in the midlatitudes and 6.8 and 5.9 kg m−2 in the Tropics.

The reduction of the 6-h forecast rmse when assimilating SSM/I TPW is 1 kg m−2, which is a reduction of 20% in the rmse. When GOES retrievals of specific humidity are assimilated, the elective reduction is 0.6 kg m−2. It is shown that in the upper levels of the troposphere (above 600 mb), the error reduction of specific humidity is largely due to the GOES retrievals, whereas in the lower troposphere (850 and 700 mb), the reduction is mostly due to the SSM/I TPW. This emphasizes the complementarity of the information contained at different wavelengths and the advantage of using multisensor retrievals in data analysis.

Full access
Christopher Grassotti, Ross N. Hoffman, Enrique R. Vivoni, and Dara Entekhabi

Abstract

A detailed intercomparison was performed for the period January 1998–June 1999 of three different sets of rainfall observations over the watershed covered by the National Weather Service Arkansas–Red Basin River Forecast Center (ABRFC). The rainfall datasets were 1) hourly 4-km-resolution ABRFC-produced P1 estimates, 2) 15-min 2-km resolution NOWrad estimates produced and marketed by Weather Services International Corporation (WSI), and 3) conventional hourly rain gauge observations available from the operational observing network. Precipitation estimates from the three products were compared at monthly, daily, and hourly timescales for the Arkansas–Red River basin and the Illinois River basin. Results indicate that the P1 products had a higher correlation and smaller bias relative to rain gauges than did the WSI products. The fact that the P1 estimates are bias corrected using gauges themselves makes an independent assessment difficult. WSI monthly accumulations seemed to overestimate (underestimate) total rainfall relative to gauges during the warm (cold) season. WSI and P1 estimates had very good agreement overall with correlation coefficients of daily accumulations generally greater than 0.7. The P1 hourly estimates were characterized by a large proportion of extremely light rainfall rates (less than 2 mm h−1). This is likely due to the P1 bias correction algorithm's use of sparse gauge data during low-level stratiform precipitation events. Finally, analyses of mean areal precipitation, fractional coverage, and storm total rainfall for the Illinois River basin demonstrate the potential impact of these rainfall products on hydrologic models that use these precipitation estimates as meteorological forcing.

Full access
Ross N. Hoffman, Christopher Grassotti, Ronald G. Isaacs, and Thomas J. Kleespies

Abstract

Satellite emission computed tomography retrieves the temperature of the atmosphere from radiances observed at multiple viewing angles and frequencies. To the extent that it provides independent information, the use of multiple viewing angles should improve the accuracy of the retrieval. Additionally, the tomographic retrievals should be more horizontally consistent since the fields of view overlap. The present study assesses these capabilities by performing a series of simulation experiments in which two-dimensional temperature fields (XZ plane) are retrieved. Several limitations cited in previous work (by H. Fleming) are addressed by realistically treating the geometry of the sensor instantaneous field of view and by using appropriate instrumental noise levels. We have used observed atmospheric cross sections and the sensor geometry and simulation codes appropriate for the HIRS2 sensor. It is found that the tomographic approach is superior to the single angle approach in the cases studied when observational noise is 1.5 brightness temperature degrees (K) in each channel. For smaller noise levels (0.75 K) the two approaches are found to be comparable.

Full access
Christopher Grassotti, S. Mark Leidner, Jean-François Louis, and Ross N. Hoffman

Abstract

The authors report on characteristics of a rain flag derived from collocation of visible and infrared image data with rain rates over the North Atlantic Ocean obtained from microwave imagery (SSM/I) during a 3-week period (15 October 1996–2 November 1996). The rain flag has been developed as part of an effort to provide an indication of contamination by heavy rainfall in NASA scatterometer datasets. The primary results of this analysis indicate 1) that a simple albedo/infrared brightness temperature threshold is capable of flagging most of the heavy rainfall, though with a fairly high rate of false alarms, and 2) that the small difference in optimal threshold between the Tropics and midlatitudes can probably be ignored. Use of the rain flag in 12 assimilation experiments during this period showed that the number of rain-flagged wind vector cells is generally less than 1% of the number of cells. Overall, the impact from using the rain-flagged data is generally less than 5 m s−1 and localized (less than 5° of latitude and longitude). However, in some cases, the effect of excluding just one to five rain-flagged points can change the resulting analysis significantly, because their placement is critical for defining the flow along a front or some other shear-dominated environment.

Full access