Balance conditions in variational data assimilation for a high-resolution forecast model

This paper explores the role of balance relationships for background error covariance modelling as the model's grid box decreases to convective-scales. Data assimilation (DA) analyses are examined from a simplified convective-scale model and DA system (called ABC-DA) with a grid box size of 1.5km in a 2D 540km (longitude), 15km (height) domain. The DA experiments are performed with background error covariance matrices B modelled and calibrated by switching on/off linear balance (LB) and hydrostatic balance (HB), and by observing a subset of the ABC variables, namely v, meridional wind, r', scaled density (a pressure-like variable), and b', buoyancy (a temperature-like variable). Calibration data are sourced from two methods of generating proxies of forecast errors. One uses forecasts from different latitude slices of a 3D parent model (here called the `latitude slice method'), and the other uses sets of differences between forecasts of different lengths but valid at the same time (the National Meteorological Center method). Root-mean-squared errors computed over the domain from identical twin DA experiments suggest that there is no combination of LB/HB switches that give the best analysis for all model quantities. It is frequently found though that the B-matrices modelled with both LB and HB do perform the best. A clearer picture emerges when the errors are examined at different spatial scales. In particular it is shown that switching on HB in B mostly has a neutral/positive effect on the DA accuracy at `large' scales, and switching off the HB has a neutral/positive effect at `small' scales. The division between `large' and `small' scales is between 10 and 100km. Furthermore, one hour forecast error correlations computed between control parameters find that correlations are small at large scales when balances are enforced, and at small scales when balances are not enforced (ideal control parameters have zero cross correlations). This points the way to modelling B with scale-dependent balances

A simple column model to explore anticipated problems in variational assimilation of satellite observations

We investigate a simplified form of variational data assimilation in a fully nonlinear framework with the aim of extracting dynamical development information from a sequence of observations over time. Information on the vertical wind profile, w(z ), and profiles of temperature, T (z , t), and total water content, qt (z , t), as functions of height, z , and time, t, are converted to brightness temperatures at a single horizontal location by defining a two-dimensional (vertical and time) variational assimilation testbed. The profiles of T and qt are updated using a vertical advection scheme. A basic cloud scheme is used to obtain the fractional cloud amount and, when combined with the temperature field, this information is converted into a brightness temperature, using a simple radiative transfer scheme. It is shown that our model exhibits realistic behaviour with regard to the prediction of cloud, but the effects of nonlinearity become non-negligible in the variational data assimilation algorithm. A careful analysis of the application of the data assimilation scheme to this nonlinear problem is presented, the salient difficulties are highlighted, and suggestions for further developments are discussed

Assessment of environments for Mars Science Laboratory entry, descent, and surface operations

The Mars Science Laboratory mission aims to land a car-sized rover on Mars’surface and operate it for at least one Mars year in order to assess whether its field area was ever capable of supporting microbial life. Here we describe the approach used to identify, characterize, and assess environmental risks to the landing and rover surface operations. Novel entry, descent, and landing approaches will be used to accurately deliver the 900-kg rover, including the ability to sense and “fly out” deviations from a best-estimate atmospheric state. A joint engineering and science team developed methods to estimate the range of potential atmospheric states at the time of arrival and to quantitatively assess the spacecraft’s performance and risk given its particular sensitivities to atmospheric conditions. Numerical models are used to calculate the atmospheric parameters, with observations used to define model cases, tune model parameters, and validate results. This joint program has resulted in a spacecraft capable of accessing, with minimal risk, the four finalist sites chosen for their scientific merit. The capability to operate the landed rover over the latitude range of candidate landing sites, and for all seasons, was verified against an analysis of surface environmental conditions described here. These results, from orbital and model data sets, also drive engineering simulations of the rover’s thermal state that are used to plan surface operations