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Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
Resources
Readme
MIT license
Activity
Custom properties
Stars
14 stars
Watchers
3 watching
Forks
9 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.txt
Repository files navigation
Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
Resources
Readme
MIT license
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Custom properties
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9 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
Resources
Readme
MIT license
Activity
Custom properties
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
Resources
Readme
MIT license
Activity
Custom properties
Stars
14 stars
Watchers
3 watching
Forks
9 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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Latest commit
History
11 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
figs
figs
results
results
scripts
scripts
LICENSE.md
LICENSE.md
README.txt
README.txt
Repository files navigation
Data are stored in LIM/data/
- retreived using get_T_H_Taux_data.m Scripts are in LIM/scripts/
STEP 0: setp.m is a function called by many scripts to define the
environmental parameters like the scratch directory. This
might need modification if you decide to run the scripts different machines or new machines at some point.
STEP 1: create a file like "LIM_sst_ssh_taux_scpdsi.m" to define X1,
X2, X3, X4, etc... with neigs, filtering, etc...
this requires data to be in ../data/, and also the functions:
corr3D
nancorr
nc2struct
ncgetvar_names
ncstruct2ncfile
rm_mtx_mean
rm_mtx_mean3D
setp
smoothx
doEOF_filter_wrap
lagCov
And optionally (for plotting):
lon180_360
X3D_tonino34.m
STEP 2: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" to
do the stochastic forcing part (SLOW, runs in parallel by default).
This calls stoForceFn.m as stoForceFn(Xinit,B,Qscld,nQeigs,nMo,deltaT,moLen,nMoExtra).
Also handles splitting out [X1| X2| X3| X4] into individual variables.
STEP 3: Create a file like "stoForceLIM_sst_ssh_taux_scpdsi.m" as a driver
script for your LIM. This will require:
stoForceFn (a function that handles the heavy lifting and makes parallel processing possible).
Edit this to modify:
- nr (number of stochastic realizations)
- nYears (number of years per simulation)
- resultsDir (optional)
Run your StoForceLIM driver script:
-Requires user to specify limParamFile
-Will store all output in the "resulsDir."
-Will also create a matlab file called "lastRun" which will store only the "dateTag"
(this can be useful later b/c each dateTag is unique down to the minute)
IMPORTANT NOTES: 1. BE CAREFUL - This script can produce a prodigious volume of data. You can easily fill up a hard drive by setting nYears
and nr to large numbers. 2. It will also create a new directory each time it is run, unless
you specify otherwise.
3. By default, it creates netCDF files from each of the variables
used in the LIM. This could be modifed in the driver script to
only save PC time series, then with the EOFs saved in ../results,
the user could re-project each realization onto its EOF loadings.
Such an approach would save considerable storage space.
Optional Diagnostics (ENSO specific):
STEP 4: Run mkNINO34pxx_and_corrs
- you can specify the expermental ID using the variable "expid"
- or you can use the "last run" as the default
STEP 5: Run mkLIMDiagPlots.m

About
Code and data for building linear inverse model (LIM) in Matlab
Resources
Readme
MIT license
Activity
Custom properties
Stars
14 stars
Watchers
3 watching
Forks
9 forks
Report repository
Releases
Packages
Contributors
Languages