WIVERN: A new satellite concept to provide global in-cloud winds, precipitation and cloud properties.

A new satellite concept with a conically scanning W-band Doppler radar to provide in-cloud winds, together with estimates of global rainfall, snowfall and cloud properties. This paper presents a conically scanning space-borne Dopplerized 94GHz radar Earth Science mission concept, WIVERN, ‘Wind VElocity Radar Nephoscope’. WIVERN aims to provide global measurements of in-cloud winds using the Doppler shifted radar returns from hydrometeors. The conically scanning radar could provide wind data with daily revisits poleward of 50°, 50-km horizontal resolution and approximately 1km vertical resolution. The measured winds, when assimilated into weather forecasts and provided they are representative of the larger scale mean flow, should lead to further improvements in the accuracy and effectiveness of forecasts of severe weather and better focusing of activities to limit damage and loss of life. It should also be possible to characterize the more variable winds associated with local convection. Polarization diversity would be used to enable high wind speeds to be unambiguously observed; analysis indicates that artifacts associated with polarization diversity are rare and can be identified. Winds should be measurable down to 1 km above the ocean surface and 2 km over land. The potential impact of the WIVERN winds on reducing forecast errors is estimated by comparison with the known positive impact of cloud motion and aircraft winds. The main thrust of WIVERN is observing in-cloud winds, but WIVERN should also provide global estimates of ice water content, cloud cover and vertical distribution continuing the data series started by CloudSat with the conical scan giving increased coverage. As with CloudSat, estimates of rainfall and snowfall rates should be possible. These non-wind products may also have a positive impact when assimilated into weather forecasts

Control of lateral boundary conditions in four-dimensional variational data assimilation for a limited area model

The limited area model forecasting problem is a lateral boundary condition (LBC) problem in addition to the initial condition problem. The data assimilation has traditionally been considered as a process for estimation of the initial condition only, while for the limited area data assimilation this estimation may be extended to include also the LBCs, at least during the data assimilation time window when observations are available. A procedure for such a control of the LBCs has been included in the four-dimensional variational data assimilation (4D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system. A description of this procedure is provided together with results from idealised as well as real data experiments. The results indicate that control of LBCs may be important with small forecast domains and in particular for weather disturbances moving quickly into and through the forecast domain