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1 \section{The ECCO state estimation cost function DRAFT!!!
2 \label{sectioneccocost}}
3
4 The current ECCO state estimation covers an $nYears = 11$ year
5 model trajectory.
6 A variety of data sets enter a least squares cost function,
7 in addition to penalty terms which constrain deviations
8 of control variables beyound their a priori errors.
9
10 \subsection{Sea surface height from TOPEX/Poseidon and ERS-1/2 altimetry}
11
12 Altimetric SSH contributions from T/P and ERS-1/2 are four-fold:
13 %
14 \begin{enumerate}
15 %
16 \item
17 an $nYears$ time mean SSH misfit between
18 model and T/P
19 %
20 \item
21 daily SSH anomaly misfits between T/P and model
22 %
23 \item
24 daily SSH anomaly misfits between ERS-1/2 and model
25 %
26 \item
27 daily absolute SSH misfit between T/P and model,
28 weighted by the full geoid error covariance.
29 %
30 \end{enumerate}
31
32 \subsubsection{Input fields}
33 ~
34
35 \begin{table}[h!]
36 \begin{center}
37 \begin{tabular}{lllc}
38 \hline \hline
39 ~&~&~&~\\
40 field & file name & deccription & unit \\
41 ~&~&~&~\\
42 \hline
43 ~&~&~&~\\
44 {\it psbar} & {\tt psbarfile} & daily model mean SSH fields & [m] \\
45 {\it tpmean} & {\tt topexmeanfile} & $nYears$ T/P mean & [cm] \\
46 {\it tpobs} & {\tt topexfile} & daily T/P SSH anomalies & [cm] \\
47 {\it erspobs} & {\tt ersfile} & daily ERS-1/2 SSH anomalies & [cm] \\
48 {\it wp} & {\tt geoid\_errfile} & diagonal of geoid error covariance & [m] \\
49 {\it wtp, wers} & {\tt ssh\_errfile} & rms of SSH anomalies & [cm] \\
50 ~&~&~&~\\
51 \hline \hline
52 \end{tabular}
53 \end{center}
54 \end{table}
55
56
57 \subsubsection{\textit{\textbf{nYears}} time mean SSH misfit}
58
59 \begin{enumerate}
60 %
61 \item
62 Compute 11yr model mean spatial distribution
63 %
64 \begin{equation}
65 psmean(i,j)\, =\,
66 \frac{1}{nDaysRec} \sum_{i=1}^{nDaysRec}
67 psbar(i,j)
68 \end{equation}
69 %
70 \item
71 Compute global offset between 11-yr model and T/P mean:
72 %
73 \begin{equation}
74 \begin{split}
75 offset & = \, \overline{tpmean} \, - \, \overline{psmean} \\
76 ~ & = \, \frac{1}{normaliz.} \sum_{i,j}
77 \left\{ tpmean(i,j) \, - \, psmean(i,j) \right\}
78 \cdot cosphi(i,j) \cdot tpmeanmask(i,j)
79 \end{split}
80 \end{equation}
81 %
82 \item
83 Misfits are computed w.r.t. global $offset$.
84 \\
85 First spatial distribution:
86 %
87 \begin{equation}
88 \begin{split}
89 cost\_ssh\_mean(i,j) & = \,
90 \frac{1}{wp^2} \left\{ \,
91 \left[ \, psmean(i,j) - \overline{psmean} \, \right] \, - \,
92 \left[ \, tpmean(i,j) - \overline{tpmean} \, \right] \, \right\}^2 \\
93 ~ & = \, \frac{1}{wp^2} \left\{ \,
94 psmean(i,j) \, - \, tpmean(i,j) \, + \, offset \, \right\}^2
95 \end{split}
96 \end{equation}
97
98 %
99 Finally, sum over all spatial entries:
100 \begin{equation}
101 \overline{cost\_ssh\_mean} \, = \,
102 \sum_{i,j} cost\_ssh\_mean(i,j)
103 \end{equation}
104
105
106
107 \end{enumerate}
108
109 \subsubsection{Misfit of daily SSH anomalies}
110
111 Computation is same for T/P and ERS-1/2.
112 Here we write out computation for T/P.
113
114 \begin{enumerate}
115 %
116 \item
117 Compute difference in anomalies:
118
119 \begin{equation}
120 \begin{split}
121 cost\_ssh\_anom(i,j,t) & = \, \frac{1}{wtp^2} \left\{ \,
122 \left[ \, psbar(i,j,t) - psmean(i,j) \, \right] \, - \,
123 \left[ \, tpobs(i,j,t) \, \right] \,
124 \right\}^2
125 \end{split}
126 \end{equation}
127 %
128 where $t$ denotes time (day) index, and
129 where it is assumed that $ nYears$ mean T/P spatial distribution
130 $tpmean(i,j)$ has already been removed from data $tpobs(i,j)$!
131
132 \item
133 Sum over all spatial points and all times
134
135 \begin{equation}
136 \begin{split}
137 \overline{cost\_ssh\_anom} & = \, \sum_{t} \sum_{i,j}
138 cost\_ssh\_anom(i,j,t)
139 \end{split}
140 \end{equation}
141
142 \end{enumerate}
143
144 \subsubsection{Flow chart}
145
146 \begin{verbatim}
147
148 cost_ssh
149 |
150 |- < compute nYears model mean >
151 |
152 |- < read nYears T/P mean >
153 | CALL COST_READTOPEXMEAN
154 |
155 |- < compute global T/P vs. model offset >
156 |
157 |- < compute cost_hmean >
158 | CALL COST_SSH_MEAN
159 |
160 |- < ... >
161
162 \end{verbatim}
163
164 \subsubsection{Weights and notes}
165
166 \begin{itemize}
167 %
168 \item
169 All data are currently masked to zero where less than 13 depth levels,
170 mimicing no contribution for depth less than 1000m.
171 %
172 \item
173 $cosphi$ term in weights is set to 1.
174 %
175 \item
176 bad T/P and ERS-1/2 values are flagged $ \le \, -9990. $
177 %
178 \item
179 T/P and ERS-1/2 data $ \le \, 1.\exp^{-8}$ cm are flagged as bad values
180 %
181 \item
182 $wp$ is read from {\tt geoid\_errfile}
183 and $1/wp^2$ is pre-computed in {\tt ecco\_cost\_weights}
184 %
185 \end{itemize}
186
187 \paragraph{$wp$ for SSH mean misfit} ~
188
189 $1/wp^2$ is pre-computed in {\tt ecco\_cost\_weights}; \\
190 $wp$ is read from {\tt geoid\_errfile};
191
192 \paragraph{$wtp$ and $wers$ for SSH anomaly misfit} ~
193
194 $1/wtp^2$, $1/wers^2$ are pre-computed in {\tt ecco\_cost\_weights}; \\
195 %
196 \begin{itemize}
197 %
198 \item
199 $wtp$, $wers$ are read from single {\tt ssh\_errfile}
200 %
201 \item
202 both are converted to meters and halved \\
203 $ wtp \, \longrightarrow \, wtp \cdot 0.01 \cdot 0.5 $
204 %
205 \item
206 ERS error is set to T/P error + 5cm \\
207 $ wers \, = \, wtp \, + 0.5cm $
208 %
209 \end{itemize}
210
211 \subsubsection{Cost diagnostics}
212
213 \begin{itemize}
214 %
215 \item
216 Map out $ cost\_ssh\_mean(i,j) $
217 %
218 \item
219 Map out $ cost\_ssh\_anom(i,j,t) $ averaged over 1 month, i.e.
220 \[
221 \frac{1}{\text{monthly entries}} \sum_{t}^{monthly} cost\_ssh\_anom(i,j,t)
222 \]
223 %
224 \item
225 sum over daily entries and plot daily average as function of time. i.e.
226 \[
227 \frac{1}{\text{daily entries}} \sum_{i,j} cost\_ssh\_anom(i,j,t)
228 \]
229 \end{itemize}
230
231 \subsection{Hydrographic constraints}
232
233 Observation of temperature and salinity from various sources are
234 used to constrain the model. These are:
235 %
236 \begin{enumerate}
237 %
238 \item
239 CTD obs. for $T$, $S$ from various WOCE sections
240 %
241 \item
242 XBT obs. for $T$
243 %
244 \item
245 Sea surface temperature (SST) and salinity (SSS) from
246 Reynolds et al. (???)
247 %
248 \item
249 $T$, $S$ from ARGO floats
250 %
251 \item
252 $T$, $S$ from fields from Levitus (???)
253 %
254 \end{enumerate}
255
256 \subsubsection{Input fields}
257 ~
258
259 \begin{table}[h!]
260 \begin{center}
261 \begin{tabular}{lllc}
262 \hline \hline
263 ~&~&~&~\\
264 field & file name & deccription & unit \\
265 ~&~&~&~\\
266 \hline
267 ~&~&~&~\\
268 {\it tbar} & {\tt tbarfile} & monthly model mean pot. temperature &
269 [$^{\circ}$C] \\
270 {\it sbar} & {\tt sbarfile} & monthly model mean salinity &
271 [ppt] \\
272 {\it tdat} & {\tt tdatfile} & monthly mean Levitus pot. temperature &
273 [$^{\circ}$C] \\
274 {\it sdat} & {\tt sdatfile} & monthly mean Levitus salinity &
275 [ppt] \\
276 {\it ctdtobs} & {\tt ctdtfile} & monthly WOCE CTD pot. temperature &
277 [$^{\circ}$C] \\
278 {\it ctdsobs} & {\tt ctdsfile} & monthly WOCE CTD salinity &
279 [ppt] \\
280 {\it xbtobs} & {\tt xbtfile} & monthly XBT in-situ(!) temperature &
281 [$^{\circ}$C] \\
282 {\it sstdat} & {\tt sstdatfile} & monthly Reynolds pot. SST &
283 [$^{\circ}$C] \\
284 {\it sssdat} & {\tt sssdatfile} & monthly Reynolds SSS &
285 [ppt] \\
286 {\it argotobs} & {\tt argotfile} & monthly ARGO in-situ(!) temperature &
287 [$^{\circ}$C] \\
288 {\it argosobs} & {\tt argosfile} & monthly ARGO salinity &
289 [ppt] \\
290 {\it wti, wsi} & {\tt data\_errfile} & vert. stdev. profile for $T$, $S$ &
291 ~ \\
292 {\it wtheta2} & {\tt temperrfile} & ~ & [$^{\circ}$C] \\
293 {\it wsalt2} & {\tt salterrfile} & ~ & [ppt] \\
294 ~&~&~&~\\
295 \hline \hline
296 \end{tabular}
297 \end{center}
298 \end{table}
299
300 \subsubsection{XBT data}
301
302 \begin{equation}
303 \begin{split}
304 cost\_xbt\_t(i,j) & = \,
305 \left[ \, \frac{cosphi \cdot mask}{wtheta2} \sum_{\tau=1}^{nMonsRec}
306 \left\{ Tbar(\tau) \, - \, T2\theta[xbtobs(\tau)] \right\}^2 \, \right](i,j)
307 \\
308 \end{split}
309 \end{equation}
310
311 \subsubsection{WOCE CTD data}
312
313 \begin{equation}
314 \begin{split}
315 cost\_ctd\_t(i,j) & = \,
316 \left[ \, \frac{cosphi \cdot mask}{wtheta2} \sum_{\tau=1}^{nMonsRec}
317 \left\{ Tbar(\tau) \, - \, ctdTobs(\tau) \right\}^2 \, \right](i,j)
318 \\
319 cost\_ctd\_s(i,j) & = \,
320 \left[ \, \frac{cosphi \cdot mask}{wsalt2} \sum_{\tau=1}^{nMonsRec}
321 \left\{ Sbar(\tau) \, - \, ctdSobs(\tau) \right\}^2 \, \right](i,j)
322 \\
323 \end{split}
324 \end{equation}
325
326 \subsubsection{ARGO float data}
327
328 \begin{equation}
329 \begin{split}
330 cost\_argo\_t(i,j) & = \,
331 \left[ \, \frac{cosphi \cdot mask}{wtheta2} \sum_{\tau=1}^{nMonsRec}
332 \left\{ Tbar(\tau) \, - \, T2\theta[argoTobs(\tau)] \right\}^2 \, \right](i,j)
333 \\
334 cost\_argo\_s(i,j) & = \,
335 \left[ \, \frac{cosphi \cdot mask}{wsalt2} \sum_{\tau=1}^{nMonsRec}
336 \left\{ Sbar(\tau) \, - \, argoSobs(\tau) \right\}^2 \, \right](i,j)
337 \\
338 \end{split}
339 \end{equation}
340
341 \subsubsection{Reynolds sea surface T, S data}
342
343 \begin{equation}
344 \begin{split}
345 cost\_sst(i,j) & = \,
346 \left[ \, \frac{cosphi \cdot mask}{wsst} \sum_{\tau=1}^{nMonsRec}
347 \left\{ Tbar(\tau) \, - \, sstDat(\tau) \right\}^2 \, \right](i,j)
348 \\
349 cost\_sss(i,j) & = \,
350 \left[ \, \frac{cosphi \cdot mask}{wsss} \sum_{\tau=1}^{nMonsRec}
351 \left\{ Sbar(\tau) \, - \, sssDat(\tau) \right\}^2 \, \right](i,j)
352 \\
353 \end{split}
354 \end{equation}
355
356 \subsubsection{Levitus montly T, S climatological data}
357
358
359 \subsubsection{Weights and notes}
360
361 \begin{itemize}
362 %
363 \item
364 $T2\theta$ is an operator mapping in-situ to potential temperatures
365 %
366 \item
367 Latitudinal weight not used:
368 \[
369 cosphi(i,j) \, = \, 1
370 \]
371 %
372 \item
373 Spatially constant weights:
374 %
375 \begin{enumerate}
376 %
377 \item
378 Read standard deviation fields \\
379 $ {\tt data\_errfile} \, \longrightarrow \,
380 wti(k), \,\, wsi(k) $ \\
381 $ {\tt data\_errfile} \, \longrightarrow \,
382 ratio = 0.25 = \left( \frac{1}{2} \right)^2 $
383 %
384 \item
385 Take inverse squares:
386 \[
387 \begin{split}
388 wtheta(k) & = \, \frac{ratio}{wti(k) \cdot wti(k)} \\
389 wsalt(k) & = \, \frac{ratio}{wsi(k) \cdot wsi(k)} \\
390 \end{split}
391 \]
392 %
393 \end{enumerate}
394 %
395 \item
396 Spatially varying weights:
397 %
398 \begin{enumerate}
399 %
400 \item
401 Read standard deviation fields \\
402 $ {\tt temperrfile} \, \longrightarrow \, wtheta2(i,j,k) $ \\
403 $ {\tt salterrfile} \, \longrightarrow \, wsalt2(i,j,k) $ \\
404 %
405 \item
406 Weights are combination of spatially constant and varying parts:
407 \[
408 \begin{split}
409 wtheta2(i,j,k) & = \, \frac{ratio}
410 {wti(k) \cdot wti(k) \, + \,wtheta2(i,j,k) \cdot wtheta2(i,j,k) } \\
411 wsalt2(i,j,k) & = \,
412 \frac{ratio}
413 {wsi(k) \cdot wsi(k) \, + \,wsalt2(i,j,k) \cdot wsalt2(i,j,k) } \\
414 \end{split}
415 \]
416 %
417 \end{enumerate}
418 %
419 \item
420 Sea surface $T$, $S$ weights:
421 \begin{itemize}
422 \item
423 SST: $ wsst \, = \, wtheta(1)$: horizontally constant
424 \item
425 SSS: $ wsss \, = \, wsalt2(i,j,1)$: horizontally varying
426 \end{itemize}
427 (Why this difference? I don't know.)
428 %
429 \end{itemize}
430
431
432 \subsubsection{Diagnostics}
433
434 \begin{itemize}
435 %
436 \item
437 Map out $wtheta2(i,j)$, $wsalt2(i,j)$.
438
439 %
440 \end{itemize}
441

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