1 / 11

Analysis of existing formulations of regression SST algorithms

Analysis of existing formulations of regression SST algorithms. Methodology. Coefficients for all equations were derived from a single dataset of matchups (MDS), between Aqua MODIS (BTs, VZA, Tref ) and iQuam SST from 28 Aug - 19 Sep 2011 – Dependent MDS

nantai
Download Presentation

Analysis of existing formulations of regression SST algorithms

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. Analysis of existing formulations of regression SST algorithms

  2. Methodology • Coefficients for all equations were derived from a single dataset of matchups (MDS), between Aqua MODIS (BTs, VZA, Tref) and iQuam SST from 28 Aug - 19 Sep 2011 – Dependent MDS • Statistics of SST wrt in situ SST were analyzed for the above MDS and also for an independent Aqua-MODIS MDS collected from 1 Mar - 30 Apr 2012 (the same MDS for all equations) – Independent MDS

  3. Nighttime SST equations 1. ACSPO: SST = a0 + a1*T11 + a2*T37+a3*T12+a4* (T37 – T12) *S θ + a5* S θ, S θ=1/sec(θ)-1 2. OSI-SAF: SST = (a1 + a2 S θ) T37 + (a3 + a4S θ) (T11 - T12) + a5S θ + a0 4. NASA-MODIS: SST = a0 + a1*T39 + a2* (T39 – T40) + a3*S θ 5. IDPS_C: SST = a0 + a1*T11 + a2* (T37 – T12) *(Ts0-273.15) + a3*S θ • 6. IDPS_K: • SST = a0 + a1*T11 + a2* (T37 – T12) *Ts0 + a3*S θ 6. NAVOCEANO – AVHRR,VIIRS: Similar to IDPS_C • 7. NAVOCEANO – GOES: • SST = a0 + a1*T11 + a2* (T39– T11) *Ts0 + a3*S θ Note three angular terms in one equation Cannot reproduce the nighttime MODIS algorithm - these bands are not saved in our MDS, and not available on VIIRS • Two versions of IDPS algorithm were analyzed • using Ts0 in K (IDPS_C) • using Ts0-273.15 (IDPS_K)

  4. Nighttime statistics of “MODIS minus in situ” SST for Dependent and Independent MDS Incremental correlation is correlation between (retrieved SST –Ts0) and (in situ SST - Ts0) • OSI-SAF delivers smallest RMSE, likely due to using a more complete set of angular terms. Independent MDS suggest that many angular terms do not cause instability in retrieved SST • IDPS_K is outperformed by ACSPO (MCSST formulation works better) • IDPS_C is less accurate than IDPS_K

  5. Independent MDS 1 Mar- 30 Apr 2012: nighttime statistics “MODIS minus in situ SST” vs. VZA • The difference between ACSPO, OSI-SAF and IDPS_K increases towards scan edges • OSI-SAF does not show instability due to using a complete set of angular terms – only improvements

  6. Daytime SST equations (1) • 1. ACSPO: • SST = a0 + a1*T11 + a2* ΔT* (Ts0 – 273.15)+ a3* Δ T*Sθ, • S θ=1/sec(θ)-1, Ts0 in K, ΔT= T11 - T12 • 2. OSI-SAF: • SST = (a1 + a2 S θ) T11 + [a3 + a4(Ts0 – 273.15) + a5 S θ] ΔT + a6Sθ + a0, • 3. IDPS_C: • ΔT <= 0.6 SST = a00 + a01*T11 + a02*ΔT* (Ts0 – 273.15) + a03* Δ T*Sθ ΔT >= 1.0 SST = a10 + a11*T11 + a12* ΔT* (Ts0 – 273.15) + a13*Δ T*Sθ • 0.6 < Δ T< 1.SSTlo = a00 + a01*T11 + a02* ΔT* (Ts0 – 273.15) + a03* Δ T*Sθ • SSThi = a10 + a11*T11 + a12* ΔT* (Ts0 – 273.15) + a13*Δ T*Sθ • SST = SSTlo + (ΔT -0.6)/(1.-0.6)*(SSThi- SSTlo) Note three angular terms in one equation Daytime IDPS and MODIS algorithms stratify coefficients into “dry” and “moist”. The difference between IDPS and MODIS is in the break-up point

  7. Daytime SST equations (2) • 4. IDPS_K: ΔT <= 0.6 SST = a00 + a01*T11 + a02*ΔT* Ts0 + a03* Δ T*SθΔT >= 1.0 SST = a10 + a11*T11 + a12* ΔT* Ts0 + a13*Δ T*Sθ 0.6 < Δ T< 1.SSTlo = a00 + a01*T11 + a02* ΔT* Ts0 + a03* Δ T*Sθ SSThi = a10 + a11*T11 + a12* ΔT* Ts0 + a13*Δ T*Sθ SST = SSTlo + (ΔT -0.6)/(1.-0.6)*(SSThi- SSTlo) • 5. MODIS_C: • ΔT <= 0.5 SST = a00 + a01*T11 + a02*ΔT*(Ts0 – 273.15) + a03* Δ T*Sθ ΔT >= 0.9 SST = a10 + a11*T11 + a12*(Ts0 – 273.15)* Ts0 + a13*Δ T*Sθ • 0.5 < Δ T< 0.9SSTlo = a00 + a01*T11 + a02* ΔT*(Ts0 – 273.15) + a03* Δ T*Sθ • SSThi = a10 + a11*T11 + a12* ΔT*(Ts0 – 273.15) + a13*Δ T*Sθ • SST = SSTlo + (ΔT -0.5)/(0.9-0.5)*(SSThi- SSTlo) Item 4 is the same as Item 3, but with Ts0 instead of Ts0-273.15 Item 5 is the same as Item 3, but with slightly different boundaries of “dry” and “moist” conditions

  8. Daytime SST equations (3) 6. MODIS_K: ΔT <= 0.5 SST = a00 + a01*T11 + a02*ΔT* Ts0 + a03* Δ T*Sθ ΔT >= 0.9 SST = a10 + a11*T11 + a12* ΔT* Ts0 + a13*Δ T*Sθ 0.5 < Δ T< 0.9SSTlo = a00 + a01*T11 + a02* ΔT* Ts0 + a03* Δ T*Sθ SSThi = a10 + a11*T11 + a12* ΔT* Ts0 + a13*Δ T*Sθ SST = SSTlo + (ΔT -0.5)/(0.9-0.5)*(SSThi- SSTlo) 7. NAVOCEANO – AVHRR, VIIRS: Similar to ACSPO 8. NAVOCEANO – GOES: SST = a0 + a1*T11 + a2*(T39 – T11) + a3* S θ Item 6 is the same as Item 4, but with slightly different boundaries between “dry” and “moist” conditions Cannot reproduce the nighttime MODIS algorithm – band 39μm is not saved in our MDS, and not available on VIIRS Two versions of IDPS and MODIS algorithms were analyzed – one using Ts0 in K (IDPS_C, MODIS_C) and another one using Ts0-273.15 (IDPS_K, MODIS_K)

  9. Daytime statistics of SST wrt in situ SST over dependent and independent MDS • OSI-SAF shows overall best performance, likely due to using a complete set of angular terms. No signs on instability due to increased number of regressors • IDPS_C and MODIS_C perform better than ACSPO, due to “dry/moist” stratification, but worse than OSI-SAF • IDPS_K and MODIS_K perform worst (they are closer to MCSST due to reduces stratification in Tref)

  10. MDS 01 March- 30 April 2012: daytime statistics of SST wrt in situ SST as functions of VZA • The difference between ACSPO, OSI-SAF, IDPS_C and MODIS_C is most significant at scan edges • IDPS_K and MODIS_K algorithms show worst performance

  11. Conclusion • The analysis of existing SST algorithms shows two ways of improvement: • Introducing new angular-dependent terms (OSI-SAF) or • Stratifying coefficients into “dry” and “moist” atmosphere (day only – IDPS, MODIS). • Using angular terms appears to outperform the dry/moist stratified algorithm during the day. Another advantage, it works during both the day and at night. • The OSI-SAF algorithm uses more predictors due to a complete set of angular terms. However, it does not show increased instability of retrieved SST, compared with other algorithms • In daytime NLSST, TS0 should be used in C rather than in K • At night, MCSST shows best performance. Should NLSST be used at night, TS0 in K should be preferred over C, to reduce sensitivity to first-guess SST • The OSI-SAF regression algorithm shows overall best performance, out of all tested. However, it still shows significant increase in RMSE towards scan edges. Further improvement can be achieved by using incremental (hybrid) algorithm, employing RTM simulations.

More Related