Clara S. Draper

An EKF assimilation of AMSR‐E soil moisture into the ISBA land surface scheme

[1]xa0An Extended Kalman Filter (EKF) for the assimilation of remotely sensed near-surface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Meteo-Frances Aire Limitee Adaptation Dynamique developpement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providing initial conditions for NWP. The EKF is used to assimilate near-surface soil moisture observations retrieved from C-band Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures into ISBA. The EKF can translate near-surface soil moisture observations into useful increments to the root-zone soil moisture. If the observation and model soil moisture errors are equal, the Kalman gain for the root-zone soil moisture is typically 20–30%, resulting in a mean net monthly increment for July 2006 of 0.025 m3 m−3 over ALADINs European domain. To test the benefit of evolving the background error, the EKF is compared to a Simplified EKF (SEKF), in which the background errors at the time of the analysis are constant. While the Kalman gains for the EKF and SEKF are derived from different model processes, they produce similar soil moisture analyses. Despite this similarity, the EKF is recommended for future work where the extra computational expense can be afforded. The method used to rescale the near-surface soil moisture data to the model climatology has a greater influence on the analysis than the error covariance evolution approach, highlighting the importance of developing appropriate methods for rescaling remotely sensed near-surface soil moisture data.

A comparison of two off‐line soil analysis schemes for assimilation of screen level observations

[1]xa0Two analysis schemes are developed within an off-line version of the land surface scheme ISBA for the initialization of soil water content and temperature in numerical weather prediction models. The first soil analysis is based on optimal interpolation that is currently operational in a number of weather centers. The second soil analysis is an extended Kalman filter (EKF) which will allow the assimilation of satellite observations. First, it is shown, by comparing the Kalman gain of both analysis schemes, that it is possible to assimilate screen level temperature and relative humidity in an off-line system. This is of great interest for future combined assimilations of conventional and satellite data. The reduced computing time in running the land surface scheme outside the atmospheric model makes Kalman filter approaches compatible with operational requirements. The methodology for coupling the land surface data assimilation with the atmospheric analysis system is explained in order to highlight the existing feedbacks between the two systems (in comparison to fully decoupled land data assimilation systems). The linearity of the observation operator Jacobians estimated by finite differences and the relevance of the soil prognostic variables to be initialized are assessed. Finally, the two systems are compared over western Europe for the month of July 2006 by assimilating screen level temperature and relative humidity every 6 h. The EKF has been simplified by keeping the covariance matrix of background errors constant. The two soil analysis schemes behave similarly in response to screen level atmospheric errors. The EKF is superior in identifying situations where the near-surface atmosphere is sensitive to soil perturbations, which leads to better use of observations. Over France, the capability of both systems to moisten the soil when rain events are absent from the forcing is demonstrated.

Root zone soil moisture from the assimilation of screen‐level variables and remotely sensed soil moisture

[1]xa0In most operational NWP models, root zone soil moisture is constrained using observations of screen-level temperature and relative humidity. While this generally improves low-level atmospheric forecasts, it often leads to unrealistic model soil moisture. Consequently, several NWP centers are moving toward also assimilating remotely sensed near-surface soil moisture observations. Within this context, an EKF is used to compare the assimilation of screen-level observations and near-surface soil moisture data from AMSR-E into the ISBA land surface model over July 2006. Several issues regarding the use of each data type are exposed, and the potential to use the AMSR-E data, either in place of or together with the screen-level data, is examined. When the two data types are assimilated separately, there is little agreement between the root zone soil moisture updates generated by each, indicating that for this experiment the AMSR-E data could not have replaced the screen-level data to obtain similar surface turbulent fluxes. For the screen-level variables, there is a persistent diurnal cycle in the model-observations bias, which is not related to soil moisture. The resulting diurnal cycle in the analysis increments demonstrates how assimilating screen-level observations can lead to unrealistic soil moisture updates, reinforcing the need to assimilate alternative data sets. However, when the two data types are assimilated together, the near-surface soil moisture provides a much weaker constraint of the root zone soil moisture than the screen-level observations do, and the inclusion of the AMSR-E data does not substantially change the results compared to the assimilation of screen-level variables alone.

The Atmospheric Water Balance over the Semiarid Murray–Darling River Basin

Abstract The atmospheric water balance over the semiarid Murray–Darling River basin in southeast Australia is analyzed based on a consecutive series of 3- to 24-h NWP forecasts from the Australian Bureau of Meteorology’s Limited Area Prediction System (LAPS). Investigation of the LAPS atmospheric water balance, including comparison of the forecast precipitation to analyzed rain gauge observations, indicates that the LAPS forecasts capture the general qualitative features of the water balance. The key features of the atmospheric water balance over the Murray–Darling Basin are small atmospheric moisture flux divergence (at daily to annual time scales) and extended periods during which the atmospheric water balance terms are largely inactive, with the exception of evaporation, which is consistent and very large in summer. These features present unique challenges for NWP modeling. For example, the small moisture fluxes in the basin can easily be obscured by the systematic errors inherent in all NWP models. Fo...