Algorithm Theoretical Based Document (ATBD) - CESBIO

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SO-TN-ESL-SM-GS-0001 Issue 1.a Date: 31/08/2006 SMOS level 2 processor Soil moisture <strong>ATBD</strong> 3.2.2.3.2 Auxiliary data resampling The description of algorithms require data to be available either at the DGG and/or DFFG scale depending of their use, We will assume in the rest of this document that these data are available to the right grid scale when needed. For example, if an ECMWF parameter is needed for a given DFFG cell or a given DGG node, we will assume we have it. Consequently, resampling or interpolating are necessary tasks, but they are not detailed in this document. Details concerning each source of auxiliary data and its specific generation method are addressed in TGRD. The purpose of this section is to provide indications and/or recommendations. Concerning spatial resampling or interpolation, we have only to consider the following possibilities concerning the spatial resolution of auxiliary data: • It is coarser than the DFFG, but not needed at DFFG scale. This is only the case for atmosphere contribution that is accounted at the DGG scale. For those data we recommend a bi-cubic scheme. That concerns surface pressure, Water vapour column content and T2m for atmosphere. • It is coarser than the DFFG and used at DFFG scale. This is the case for most ECMWF data as well as soil texture data. We recommend disaggregating the auxiliary data cell by replicating their contents to the DFFG (actually also a "nearest" scheme). A special mention about Snow which will be redistributed by filling the highest latitude part of each fraction (unless we can use altitude as well!) • It is close to the DFFG resolution but defined on a different grid. This could be the case for LAI or topography … We need to resample those auxiliary data to the reference grid through an interpolator. We recommend that such an interpolator be a bi-cubic. There are many possible interpolating schemes. An overview for spatial interpolation can be found in a DEIMOS technical note ([31]; section 2.5, Resampling Approach). This document gives formulas and advantages / drawbacks from both computer and mathematical standpoints. Concerning time interpolation, it is not foreseen to operate a coupled space and time interpolation i.e. to interpolate evolving auxiliary data values at each space and time SMOS measurement. Besides, we should also deal with the multitemporal characteristic of the L1c product that is made up with multiple snapshots measurements at different observing time. For now it concerns only evolving ECMWF forecast data. For some smooth parameter like pressure perhaps time interpolation could be done. But for others, like snow, the meaning or the validity of such an approach would be doubtful. So we recommend using a nearest neighbour time scheme applied to each half orbit as a whole, based on the UTC time when crossing the equator. In a sequence of 3 hourly forecast steps we have to choose the forecast step time the closest to the half orbit UTC time when crossing the equator. Assuming that a SMOS half orbit is ~50’ long, the worst case, in time lag between the forecast step time and the SMOS observing time, occur when the UTC time of the orbit cross the equator coincide with a forecast half step time (step time plus 1h30). Using that scheme leads to a maximum time lag of 1h55’ between observation and forecast times. 3.2.2.3.3 Pre-processing in order to obtain DGG quantities • Default fractions FM 0 used by the decision tree They are static except for non permanent (NPE) frost, snow zone and possibly water events; Figure 8 (borrowed from [98]) shows the concerned areas as defined using ECOCLIMAP climatology [32] and ISLSCP-I data. It is possible to compute defaults fractions over the DGG assuming neither (NPE) snow nor frost. These default fractions will always remain valid over areas shown as blank on these maps so they can be computed offline and thus provided directly through tables in TGRD given the L1c node number. This option will save pre-processing CPU time. The fractions will be computed on the fly only if auxiliary data report snow or frozen situations or possibly flood conditions. . 69
SO-TN-ESL-SM-GS-0001 Issue 1.a Date: 31/08/2006 SMOS level 2 processor Soil moisture <strong>ATBD</strong> Figure 8 : Spatial frequency distribution of (top) snow and (bottom) soil freezing • Default reference parameter values As a consequence of the aforementioned, it is thus possible to apply the decision tree offline for each fraction over the SMOS grid assuming neither NPE snow nor frost. In those cases, all the reference values coming from static auxiliary data and model selection will always remain valid over areas shown as blank on these maps and can be provided directly trough TGRD tables, given the L1c node number. This option will save some CPU time, since applying the decision tree is lighter than computing the fraction. This might be extended to parameters which enter directly the forward models: optical depths, soil structure… • Local DFFG working areas Cases where a single fraction is present can be identified from the default fraction computation presented above, over zones where NPE features do not occur. In other cases, local subsets of DFFG arrays are needed around the DGG node being processed. The local subset is a square of size = WEF_SIZE made up with DFFG cells that defines the DFFG working area, WA DFFG . The suggested value (WEF_SIZE=123 km) corresponds to twice the largest 3dB footprint occurring in SMOS soil moisture observations. One side of the square can be taken parallel to the local meridian. 3.2.2.4 Obtaining the incidence angle dependent weighting function WEF In the general case, weighting functions are necessary over land surfaces for 3 purposes: . 70
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Algorithm Theoretical Based Document (ATBD) - CESBIO

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