Ross N. Hoffman

A multiyear global surface wind velocity dataset using SSM/I wind observations

The Special Sensor Microwave Imagers (SSM/I) aboard three DMSP satellites have improved a large dataset of surface wind speeds over the global oceans from July 1987 to the present. These data are characterized by high resolution, coverage, and accuracy, but their application has been limited by the lack of directional information. In an effort to extend the applicability of these data , methodology has been developed to assign directions to the SSM/I wind speeds and to produce analyses using these data. Following extensive testing, this methodology has been used to generate a seven and one-half year dataset (from July 1987 through December 1994) of global SSM/I wind vectors. These data are currently being used in a variety of atmospheric and oceanic applications and are available to interested investigators. Recent results presented in this paper show the accuracy of the SSM/I wind velocities, the ability of these data to improve surface wind analyses, and the propagation of a synoptic-scale convergent cortex in the Tropics that can be tracked from year to year in annual mean SSM/I wind fields. 11 refs., 5 figs., 2 tabs.

High-Latitude Ocean and Sea Ice Surface Fluxes: Challenges for Climate Research

Polar regions have great sensitivity to climate forcing; however, understanding of the physical processes coupling the atmosphere and ocean in these regions is relatively poor. Improving our knowledge of high-latitude surface fluxes will require close collaboration among meteorologists, oceanographers, ice physicists, and climatologists, and between observationalists and modelers, as well as new combinations of in situ measurements and satellite remote sensing. This article describes the deficiencies in our current state of knowledge about air–sea surface fluxes in high latitudes, the sensitivity of various high-latitude processes to changes in surface fluxes, and the scientific requirements for surface fluxes at high latitudes. We inventory the reasons, both logistical and physical, why existing flux products do not meet these requirements. Capturing an annual cycle in fluxes requires that instruments function through long periods of cold polar darkness, often far from support services, in situations subject to icing and extreme wave conditions. Furthermore, frequent cloud cover at high latitudes restricts the availability of surface and atmospheric data from visible and infrared (IR) wavelength satellite sensors. Recommendations are made for improving high-latitude fluxes, including 1) acquiring more in situ observations, 2) developing improved satellite-flux-observing capabilities, 3) making observations and flux products more accessible, and 4) encouraging flux intercomparisons.

Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting

Abstract The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nes...

Application of satellite surface wind data to ocean wind analysis

A new set of cross-calibrated, multi-satellite ocean surface wind data is described. The principal data set covers the global ocean for the period beginning in 1987 with six-hour and 25-km resolution, and is produced by combining all ocean surface wind speed observations from SSM/I, AMSR-E, and TMI, and all ocean surface wind vector observations from QuikSCAT and SeaWinds. An enhanced variational analysis method (VAM) performs quality control and combines these data with available conventional ship and buoy data and ECMWF analyses. The VAM analyses fit the data used very closely and contain small-scale structures not present in operational analyses. Comparisons with withheld WindSat observations are also shown to be very good. These data sets should be extremely useful to atmospheric and oceanic research, and to air-sea interaction studies.

Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model

Abstract This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locat...

CONTROLLING THE GLOBAL WEATHER

Abstract The earths atmosphere may be chaotic and very likely is sensitive to small perturbations. Certainly, very simple nonlinear dynamical models of the atmosphere are chaotic, and the most realistic numerical weather prediction models arevery sensitive to initial conditions. Chaos implies that there is a finite predictability time limit no matter how well the atmosphere is observed and modeled. Extreme sensitivity to initial conditions suggests that small perturbations tothe atmosphere may effectively control the evolution of the atmosphere, if the atmosphere is observed and modeled sufficiently well. The architecture of a system to control the global atmosphere and the components of such a system are described. A feedback control system similar to many used in industrial settings is envisioned. Althoughthe weather controller is extremely complex, the existence of the required technology is plausible in the time range of several decades. While the concept of controlling the weather has often appeared...

An estimate of increases in storm surge risk to property from sea level rise in the first half of the twenty-first century.

Abstract Sea level is rising as the World Ocean warms and ice caps and glaciers melt. Published estimates based on data from satellite altimeters, beginning in late 1992, suggest that the global mean sea level has been rising on the order of 3 mm yr−1. Local processes, including ocean currents and land motions due to a variety of causes, modulate the global signal spatially and temporally. These local signals can be much larger than the global signal, and especially so on annual or shorter time scales. Even increases on the order of 10 cm in sea level can amplify the already devastating losses that occur when a hurricane-driven storm surge coincides with an astronomical high tide. To quantify the sensitivity of property risk to increasing sea level, changes in expected annual losses to property along the U.S. Gulf and East Coasts are calculated as follows. First, observed trends in sea level rise from tide gauges are extrapolated to the year 2030, and these changes are interpolated to all coastal location...

Multiple-Timescale Intercomparison of Two Radar Products and Rain Gauge Observations over the Arkansas–Red River Basin

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 21). 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.

Evaluating the effects of image filtering in short-term radar rainfall forecasting for hydrological applications

Radar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short-term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas-Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple ‘persistence’ and slightly better than ‘uniform advection’. At the scale of a large hydrologic basin (∼ 6000 km 2 ), filter-based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment-based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated.

A Simulation Study Using a Local Ensemble Transform Kalman Filter for Data Assimilation in New York Harbor

Data assimilation approaches that use ensembles to approximate a Kalman filter have many potential advantages for oceanographic applications. To explore the extent to which this holds, the Estuarine and Coastal Ocean Model (ECOM) is coupled with a modern data assimilation method based on the local ensemble transform Kalman filter (LETKF), and a series of simulation experiments is conducted. In these experiments, a long ECOM “nature” run is taken to be the “truth.” Observations are generated at analysis times by perturbing the nature run at randomly chosen model grid points with errors of known statistics. A diverse collection of model states is used for the initial ensemble. All experiments use the same lateral boundary conditions and external forcing fields as in the nature run. In the data assimilation, the analysis step combines the observations and the ECOM forecasts using the Kalman filter equations. As a control, a free-running forecast (FRF) is made from the initial ensemble mean to check the relative importance of external forcing versus data assimilation on the analysis skill. Results of the assimilation cycle and the FRF are compared to truth to quantify the skill of each. The LETKF performs well for the cases studied here. After just a few assimilation cycles, the analysis errors are smaller than the observation errors and are much smaller than the errors in the FRF. The assimilation quickly eliminates the domain-averaged bias of the initial ensemble. The filter accurately tracks the truth at all data densities examined, from observations at 50% of the model grid points down to 2% of the model grid points. As the data density increases, the ensemble spread, bias, and error standard deviation decrease. As the ensemble size increases, the ensemble spread increases and the error standard deviation decreases. Increases in the size of the observation error lead to a larger ensemble spread but have a small impact on the analysis accuracy.