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Global one-meter soil moisture fields from satellellite

Global one-meter soil moisture fields from satellellite. Ralf Lindau. Total variance External variance Internal variance = Variance between + Mean variance

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Global one-meter soil moisture fields from satellellite

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  1. Global one-meter soil moisture fields from satellellite Ralf Lindau Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  2. Total variance External variance Internal variance = Variance between + Mean variance the means of the within the subsamples subsamples ANOVA of Soil Moisture measurements Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  3. Local longtime means 73% of the soil moisture variance is explained by four parameters : Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  4. Temporal Anomalies • In a second step 10 Ghz measurements are used to retrieve the remaining temporal part of the variance. • A correlation of 0.609 is attained. Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  5. Two-step Retrieval Climatological mean derived from: • Longterm precipitation • Soil texture • Vegetation density • Terrain slope + Temporal anomalies from: • Brightness temperatures at 10 GHz • Anomalies of rain and air temperature Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  6. Application: DEKLIM BALTIMOS within DEKLIM (Deutsches Klimaforschungsprogramm): Validation of a 10-years climate run of the regional model REMO using SMMR. Example: Oder catchment Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  7. Application: AMSR GEOLAND within GMES (Global Monitoring for Environment and Security): Derivation of global soil moisture fields from AMSR Longterm mean Temporal anomaly Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  8. Reviewer 1 Why is spatial variance dominating? First, it seemed a bit surprising to me that not the annual cycle but the spatial variability is the largest source of variance. It is not fully clear to me that this is caused by the fact that also precipitation variability across locations is larger than the annual cycle, or whether this is (partly) an artefact of definitions of wilting point/field capacity, which are locally strongly varying. The latter source of variability is often filtered out while analysing land surface model results and/or compare these to observations (like in the Global Soil Wetness Project, GSWP), by comparing a scaled soil water content. It would be good to have a bit more insight in the origin of this dominating spatial variation. I doubt that SMMR is really useful to explain the temporal variance. Second, the (relative) contributions of various datasets is well demonstrated in the temporal mean external variability in Table 2, but a similar demonstration is missing for the temporally varying components. A similar table is actually needed to support the claim in the conclusion that SMMR data are a useful addition in this analysis. Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  9. Reviewer 2 I am not convinced (J. Fischer) that SMMR is useful. Concerning section 6: I'm not convinced, that the usefulness of passive microwave data is demonstrated. It is not shown, that the observations are better reproduced, if microwave data are considered. This can be done by excluding these data from the analysis, replotting figure 6 and 7 and comparing them with the old plots. Comparison with existing datasets is indispensable! A comparison with already existing soil moisture datasets is indispensable. Both, model based (reanalysis) and remote sensing data should be discussed (e.g. look at Dirmeyer et al., 2004, Journal of Hydrometeorology,5,1011-1033). Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  10. Global Soil Moisture Data NCEP-CPC: Model product (Climate Prediction Center) Soil water column (mm) Constant soil depth of 160 cm Constant porosity 0.475 1948 - present ERS: Satellite product from ERS-1 and ERS-2 (European Remote Sens.) Active microwave (5.3 GHz) scatterometer Soil Water Index (between wilting point and field capacity) 1992 - 2000 SMMR: Satellite plus ancillary data (rain, soil, vegetation, ...) Passive microwave (10.7 GHz) SMMR Soil water column (mm) 1979 - 1987 Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  11. Mean 1979 – 1987 CPC CPC and SMMR soil moisture patterns are in good agreement SMMR Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  12. Comparison CPC - SMMR Correlation: 0.904 Global means: 272 mm / 206 mm CPC wetter in Himalayas, Rockies, Andes CPC dryer in India, Amazonia Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  13. Soil Water Index from ERS Soil moisture given in SWI instead of water coulumn. Wettest regions in cold climate. Suspected difficulties due to permanent snow and vegetation density Comparison to CPC shows low correlation. r = 0.435 Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  14. Temporal comparison CPC – SMMR comparison for 1979 – 1987 for a grid box near Berlin - CPC wetter due to deeper soil layer. - CPC has higher variability - But: Correlation is not bad. r = 0.652 Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  15. Map of Correlations Correlations of 0.6 prevail over Europe. High up to 0.8 around Adriatic, Baltic States, South Sweden, ... Low (0.3) over the forests of Carpathians and Tatra Problems with sea ice at coast of Bothnian and Finnish Bay Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  16. Internal Pixel Variance Belarussian soil moisture data 21 stations, 10 daily during about 10 years Average spatially and compare the reduced variance to the total variance 40% are left for 400-km-Pixels 59% are left for SMMR-Pixels The left external variance is equal to the maximum correlation between point measurements and area averages Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  17. Correlation SM vs TB r = -0.810 r = -0.675 Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

  18. Outlook Cheerless. Deadline is already exceeded. Shall Dr Lindau continue anyhow or should he better complete the Financial Reports for EU-Projects or should he better finish the work he is paid for? Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007

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