1 / 21

Jeff Settle, ESSC

Reconciling Point and Area Data for RADAGAST. Jeff Settle, ESSC. GIST 24 October 2006. The problem. RADAGAST compares fluxes at the surface and at the TOA. The former are measured at a point, the latter are averaged over an area ~40kmX40km. Is that OK?

liza
Download Presentation

Jeff Settle, ESSC

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Reconciling Point and Area Data for RADAGAST Jeff Settle, ESSC GIST 24 October 2006 GIST 24/10/06

  2. The problem RADAGAST compares fluxes at the surface and at the TOA. The former are measured at a point, the latter are averaged over an area ~40kmX40km. Is that OK? Many previous attempts to estimate RBs under the ARM programme have taken the time-average of the point ARM measurements to be a sound estimate of the same time-average over a much wider area. 1 hours average at a point ~ 1hours average over a 50km square (According to ARM Wiscombe, pers.comm). GIST 24/10/06

  3. GIST 24/10/06

  4. Definition xA denotes an average of x over an area A (and a time period T). xobs is our observation, an average over a different area (a point in this case). GIST 24/10/06

  5. Similar considerations apply when: validating satellite-derived geophysical variables testing large-scale models with observational data Thus, let xpdenote a model prediction of xA. GIST 24/10/06

  6. Sampling error arises from heterogeneity in the underlying variable being averaged, caused by heterogeeity in some other underlying variable (e.g. cloud cover). The variations are characterised by: Their point variance The size of the area A The period of time averaging Correlation functions in space and time spatial scale length temporal scale time The spatial scale length defines the size of a “lump” in the underlying variable. The time scale, compared to the integration period, defines a number of semi-independent observation periods. GIST 24/10/06

  7. To keep it simple,I make the assumption: A is very large compared to the scale length(2) of the heterogeneity (GERB and clouds: 40km2 vs 4-10km2 The sampling error is then dominated by the statistical variance in the observation. GIST 24/10/06

  8. Example: How is the SW downward flux at the surface for a GERB pixel related to that at ARM. The variation in time t of this flux at a point r be written: x(r,t) = D(r,t) + d(r,t).[1-c(r,t)] (D = diffuse irradiance, d = direct irradiance) ctakes the value 0 or 1 (according to whether a cloud is intercepting the sun). GIST 24/10/06

  9. Cloud statistical model To proceed we need to consider c(r,t) as random variable, with mean m (say). Since c2= c, the expected value of c2 is also m, and so the variance of c is: Var(c) = m - m2. The instantaneous error on is: d√(µ-µ2) GIST 24/10/06

  10. GIST 24/10/06

  11. The error after averaging over time depends on the persistence of cloud obscuration, captured in the autocorrelation function of c. where CTis the correlation time. We find, for x the Shortwave downward flux: GIST 24/10/06

  12. k is shown below for two cases. Black line: simple exponential Blue line: Gaussian GIST 24/10/06

  13. GIST 24/10/06

  14. For long integration times (T>>CT), e.g. with µ = 0.5 (worst case) d ~ 300Wm-2 CT = 20 minutes We have dSW ~ 120/√T Wm-2 (T in hours) = 30 Wm-2 T = 16hr Most of the required numbers can be found directly from the time series of flux at the observation point. GIST 24/10/06

  15. Our approach for SW irradiance is: (1) to use the SEVIRI cloud mask to determine the cloud cover across the area of comparison. (2) to use data from the two ARM sites to generate time series of clear and cloudy SW irradiances. (3) combine these to provide the required area irradiance. GIST 24/10/06

  16. The theoretical first-order error is now driven by the accuracy of the cloud mask. If the mask is binary, determined by a simple threshold test, the error depends on the true distribution of cloud cover. Assume: the cloud mask predicts cloud whenever the true cloudiness is>50%, predicts clear when the true cloudiness is <50%, and that true cloudiness is uniformly distributed for partial cover. The relative error on a day’s irradiance (SHI pixel) is then: fmixed is the proportion of SEVIRI pixels not completely cloudy or completely clear. d=300Wm-2 as before, error <10Wm-2 GIST 24/10/06

  17. SW Upward/Net This is really just the shortwave-downward times (1-the albedo). The latter is quite heterogeneous, and variable in time. Left: SPOT image of Niamey, June. Right: August GIST 24/10/06

  18. Error on SW net da may be systematically in error if we use the fluxes at the AMF to define the albedo. We minimise that term by using the mean albedo in calculations. GIST 24/10/06

  19. Current status We are presently working on the cloud mask. This is needed for the conditional sampling of the various meteorological conditions at the site, as well as for the calculation of the SRB. GIST 24/10/06

  20. Conclusion Time-averaged SW fluxes at a point are a poor estimate of the time-area average over a footprint much larger than typical cloud length scales. The sampling error decreases like 1/√T (effectively, 1/√N, where N is the number of times the sun is switched on and off by clouds). SEVIRI to the rescue, provided the cloud mask is up to scratch. (not shown) LW downward fluxes less of a problem, in theory, because some spatial averaging is effectively done. GIST 24/10/06

  21. GIST 24/10/06

More Related