Index: /issm/trunk-jpl/src/m/classes/stochasticforcing.m
===================================================================
--- /issm/trunk-jpl/src/m/classes/stochasticforcing.m	(revision 27250)
+++ /issm/trunk-jpl/src/m/classes/stochasticforcing.m	(revision 27251)
@@ -52,5 +52,5 @@
 			for field=self.fields
 				%Checking agreement of classes
-				if(contains(field,'SMBautoregression'))
+				if(contains(field,'SMBarma'))
 					mdname = structstoch.mdnames(find(strcmp(structstoch.fields,char(field))));
 					if~(isequal(class(md.smb),char(mdname)))
@@ -88,5 +88,5 @@
 					end
 				end
-				if(contains(field,'BasalforcingsDeepwaterMeltingRateAutoregression'))
+				if(contains(field,'BasalforcingsDeepwaterMeltingRatearma'))
 					mdname = structstoch.mdnames(find(strcmp(structstoch.fields,char(field))));
 					if~(isequal(class(md.basalforcings),char(mdname)))
@@ -111,5 +111,5 @@
             end
 				%Checking for specific dimensions
-				if ~(strcmp(field,'SMBautoregression') || strcmp(field,'FrontalForcingsRignotAutoregression') || strcmp(field,'BasalforcingsDeepwaterMeltingRateAutoregression'))
+				if ~(strcmp(field,'SMBarma') || strcmp(field,'FrontalForcingsRignotarma') || strcmp(field,'BasalforcingsDeepwaterMeltingRatearma'))
 					checkdefaults = true; %field with non-specific dimensionality
 				end
@@ -118,51 +118,51 @@
 			%Retrieve all the field dimensionalities
 			dimensions = self.defaultdimension*ones(1,num_fields);
-			indSMBar   = -1; %about to check for index of SMBautoregression
-			indTFar	  = -1; %about to check for index of FrontalForcingsRignotAutoregression
-			indBDWar	  = -1; %about to check for index of BasalforcingsDeepwaterMeltingRateAutoregression
-			if any(contains(self.fields,'SMBautoregression'))
-				indSMBar = find(contains(self.fields,'SMBautoregression')); %index of SMBar, now check for consistency with other ar timesteps 
-				dimensions(indSMBar) = md.smb.num_basins;
-				if(md.smb.ar_timestep<self.stochastictimestep)
-					error('SMBautoregression cannot have a timestep shorter than stochastictimestep');
-				end
-			end
-			if any(contains(self.fields,'FrontalForcingsRignotAutoregression'))
-				indTFar	= find(contains(self.fields,'FrontalForcingsRignotAutoregression')); %index of TFar, now check for consistency with other ar timesteps 
-				dimensions(indTFar) = md.frontalforcings.num_basins;
-				if(md.frontalforcings.ar_timestep<self.stochastictimestep)
-					error('FrontalForcingsRignotAutoregression cannot have a timestep shorter than stochastictimestep');
-				end
-			end
-			if any(contains(self.fields,'BasalforcingsDeepwaterMeltingRateAutoregression'))
-				indBDWar	= find(contains(self.fields,'BasalforcingsDeepwaterMeltingRateAutoregression')); %index of BDWar, now check for consistency with other ar timesteps 
-				dimensions(indBDWar) = md.basalforcings.num_basins;
-				if(md.basalforcings.ar_timestep<self.stochastictimestep)
-					error('BasalforcingsDeepwaterMeltingRateAutoregression cannot have a timestep shorter than stochastictimestep');
+			indSMBarma = -1; %about to check for index of SMBarma
+			indTFarma  = -1; %about to check for index of FrontalForcingsRignotarma
+			indBDWarma  = -1; %about to check for index of BasalforcingsDeepwaterMeltingRatearma
+			if any(contains(self.fields,'SMBarma'))
+				indSMBarma = find(contains(self.fields,'SMBarma')); %index of SMBarma, now check for consistency with other arma timesteps 
+				dimensions(indSMBarma) = md.smb.num_basins;
+				if(md.smb.arma_timestep<self.stochastictimestep)
+					error('SMBarma cannot have a timestep shorter than stochastictimestep');
+				end
+			end
+			if any(contains(self.fields,'FrontalForcingsRignotarma'))
+				indTFarma	= find(contains(self.fields,'FrontalForcingsRignotarma')); %index of TFarma, now check for consistency with other arma timesteps 
+				dimensions(indTFarma) = md.frontalforcings.num_basins;
+				if(md.frontalforcings.arma_timestep<self.stochastictimestep)
+					error('FrontalForcingsRignotarma cannot have a timestep shorter than stochastictimestep');
+				end
+			end
+			if any(contains(self.fields,'BasalforcingsDeepwaterMeltingRatearma'))
+				indBDWarma	= find(contains(self.fields,'BasalforcingsDeepwaterMeltingRatearma')); %index of BDWarma, now check for consistency with other arma timesteps 
+				dimensions(indBDWarma) = md.basalforcings.num_basins;
+				if(md.basalforcings.arma_timestep<self.stochastictimestep)
+					error('BasalforcingsDeepwaterMeltingRatearma cannot have a timestep shorter than stochastictimestep');
 				end
 			end
 			size_tot = sum(dimensions);
 
-			if(indSMBar~=-1 && indTFar~=-1) %both autoregressive models are used: check autoregressive time step consistency
-				if(md.smb.ar_timestep~=md.frontalforcings.ar_timestep)
-					crossentries = reshape(self.covariance(1+sum(dimensions(1:indSMBar-1)):sum(dimensions(1:indSMBar)),1+sum(dimensions(1:indTFar-1)):sum(dimensions(1:indTFar))),1,[]);
+			if(indSMBarma~=-1 && indTFarma~=-1) %both ARMA models are used: check ARMA time step consistency
+				if(md.smb.arma_timestep~=md.frontalforcings.arma_timestep)
+					crossentries = reshape(self.covariance(1+sum(dimensions(1:indSMBarma-1)):sum(dimensions(1:indSMBarma)),1+sum(dimensions(1:indTFarma-1)):sum(dimensions(1:indTFarma))),1,[]);
 					if any(crossentries~=0)
-						error('SMBautoregression and FrontalForcingsRignotAutoregression have different ar_timestep and non-zero covariance');
-					end
-				end
-			end
-			if(indSMBar~=-1 && indBDWar~=-1) %both autoregressive models are used: check autoregressive time step consistency
-				if(md.smb.ar_timestep~=md.basalforcings.ar_timestep)
-					crossentries = reshape(self.covariance(1+sum(dimensions(1:indSMBar-1)):sum(dimensions(1:indSMBar)),1+sum(dimensions(1:indBDWar-1)):sum(dimensions(1:indBDWar))),1,[]);
+						error('SMBarma and FrontalForcingsRignotarma have different arma_timestep and non-zero covariance');
+					end
+				end
+			end
+			if(indSMBarma~=-1 && indBDWarma~=-1) %both ARMA models are used: check ARMA time step consistency
+				if(md.smb.arma_timestep~=md.basalforcings.arma_timestep)
+					crossentries = reshape(self.covariance(1+sum(dimensions(1:indSMBarma-1)):sum(dimensions(1:indSMBarma)),1+sum(dimensions(1:indBDWarma-1)):sum(dimensions(1:indBDWarma))),1,[]);
 					if any(crossentries~=0)
-						error('SMBautoregression and BasalforcingsDeepwaterMeltingRateAutoregression have different ar_timestep and non-zero covariance');
-					end
-				end
-			end
-			if(indTFar~=-1 && indBDWar~=-1) %both autoregressive models are used: check autoregressive time step consistency
-				if(md.frontalforcings.ar_timestep~=md.basalforcings.ar_timestep)
-					crossentries = reshape(self.covariance(1+sum(dimensions(1:indTFar-1)):sum(dimensions(1:indTFar)),1+sum(dimensions(1:indBDWar-1)):sum(dimensions(1:indBDWar))),1,[]);
+						error('SMBarma and BasalforcingsDeepwaterMeltingRatearma have different arma_timestep and non-zero covariance');
+					end
+				end
+			end
+			if(indTFarma~=-1 && indBDWarma~=-1) %both ARMA models are used: check ARMA time step consistency
+				if(md.frontalforcings.arma_timestep~=md.basalforcings.arma_timestep)
+					crossentries = reshape(self.covariance(1+sum(dimensions(1:indTFarma-1)):sum(dimensions(1:indTFarma)),1+sum(dimensions(1:indBDWarma-1)):sum(dimensions(1:indBDWarma))),1,[]);
 					if any(crossentries~=0)
-						error('FrontalForcingsRignotAutoregression and BasalforcingsDeepwaterMeltingRateAutoregression have different ar_timestep and non-zero covariance');
+						error('FrontalForcingsRignotarma and BasalforcingsDeepwaterMeltingRatearma have different arma_timestep and non-zero covariance');
 					end
 				end
@@ -181,5 +181,5 @@
 			disp(sprintf('   stochasticforcing parameters:'));
 			fielddisplay(self,'isstochasticforcing','is stochasticity activated?');
-			fielddisplay(self,'fields','fields with stochasticity applied, ex: [{''SMBautoregression''}], or [{''SMBforcing''},{''DefaultCalving''}]');
+			fielddisplay(self,'fields','fields with stochasticity applied, ex: [{''SMBarma''}], or [{''SMBforcing''},{''DefaultCalving''}]');
 			fielddisplay(self,'defaultdimension','dimensionality of the noise terms (does not apply to fields with their specific dimension)');
 			fielddisplay(self,'default_id','id of each element for partitioning of the noise terms (does not apply to fields with their specific partition)');
@@ -212,11 +212,11 @@
 				for field=self.fields
 					%Checking for specific dimensions
-					if(strcmp(field,'SMBautoregression'))
+					if(strcmp(field,'SMBarma'))
 						dimensions(ind) = md.smb.num_basins;
 					end
-					if(strcmp(field,'FrontalForcingsRignotAutoregression'))
+					if(strcmp(field,'FrontalForcingsRignotarma'))
 						dimensions(ind) = md.frontalforcings.num_basins;
 					end
-					if(strcmp(field,'BasalforcingsDeepwaterMeltingRateAutoregression'))
+					if(strcmp(field,'BasalforcingsDeepwaterMeltingRatearma'))
 						dimensions(ind) = md.basalforcings.num_basins;
 					end
@@ -225,5 +225,5 @@
 
 				%Scaling covariance matrix (scale column-by-column and row-by-row)
-				scaledfields = {'BasalforcingsDeepwaterMeltingRateAutoregression','BasalforcingsSpatialDeepwaterMeltingRate','DefaultCalving','FloatingMeltRate','SMBautoregression','SMBforcing'}; %list of fields that need scaling *1/yts
+				scaledfields = {'BasalforcingsDeepwaterMeltingRatearma','BasalforcingsSpatialDeepwaterMeltingRate','DefaultCalving','FloatingMeltRate','SMBarma','SMBforcing'}; %list of fields that need scaling *1/yts
 				tempcovariance = self.covariance; %copy of covariance to avoid writing back in member variable
 				for i=1:num_fields
@@ -266,5 +266,5 @@
 	% supported and corresponding md names
 	structure.fields = {...
-		'BasalforcingsDeepwaterMeltingRateAutoregression',...
+		'BasalforcingsDeepwaterMeltingRatearma',...
 		'BasalforcingsSpatialDeepwaterMeltingRate',...
 		'DefaultCalving',...
@@ -273,10 +273,10 @@
 		'FrictionCoulombWaterPressure',...
 		'FrictionSchoofWaterPressure',...
-		'FrontalForcingsRignotAutoregression',...
-		'SMBautoregression',...
+		'FrontalForcingsRignotarma',...
+		'SMBarma',...
 		'SMBforcing'
 		};
 	structure.mdnames = {...
-		'autoregressionlinearbasalforcings',...
+		'linearbasalforcingsarma',...
 		'spatiallinearbasalforcings',...
 		'calving',...
@@ -285,6 +285,6 @@
 		'frictioncoulomb',...
 		'frictionschoof',...
-		'frontalforcingsrignotautoregression',...
-		'SMBautoregression',...
+		'frontalforcingsrignotarma',...
+		'SMBarma',...
 		'SMBforcing'
 	};
Index: /issm/trunk-jpl/src/m/classes/stochasticforcing.py
===================================================================
--- /issm/trunk-jpl/src/m/classes/stochasticforcing.py	(revision 27250)
+++ /issm/trunk-jpl/src/m/classes/stochasticforcing.py	(revision 27251)
@@ -40,6 +40,6 @@
         s += '   FloatingMeltRate\n'
         s += '   FrictionWaterPressure\n'
-        s += '   FrontalForcingsRignotAutoregression (thermal forcing)\n'
-        s += '   SMBautoregression\n'
+        s += '   FrontalForcingsRignotarma (thermal forcing)\n'
+        s += '   SMBarma\n'
         s += '   SMBforcing\n'
         return s
@@ -75,5 +75,5 @@
         for field in self.fields:
             # Checking agreement of classes
-            if 'SMBautoregression' in field:
+            if 'SMBarma' in field:
                 mdname = structstoch[field]
                 if (type(md.smb).__name__ != mdname):
@@ -99,5 +99,5 @@
                 if (type(md.basalforcings).__name__ != mdname):
                     raise TypeError('md.basalforcings does not agree with stochasticforcing field {}'.format(field))
-            if 'BasalforcingsDeepwaterMeltingRateAutoregression' in field:
+            if 'BasalforcingsDeepwaterMeltingRatearma' in field:
                 mdname = structstoch[field]
                 if (type(md.basalforcings).__name__ != mdname):
@@ -115,41 +115,41 @@
 
             # Checking for specific dimensions
-            if field not in['SMBautoregression', 'FrontalForcingsRignotAutoregression','BasalforcingsDeepwaterMeltingRateAutoregression']:
+            if field not in['SMBarma', 'FrontalForcingsRignotarma','BasalforcingsDeepwaterMeltingRatearma']:
                 checkdefaults = True  # field with non-specific dimensionality
 
         # Retrieve sum of all the field dimensionalities
         dimensions = self.defaultdimension*np.ones((num_fields))
-        indSMBar   = -1  # About to check for index of SMBautoregression
-        indTFar    = -1  # About to check for index of FrontalForcingsRignotAutoregression
-        indBDWar   = -1  # About to check for index of BasalforcingsDeepwaterMeltingRateAutoregression
-        if ('SMBautoregression' in self.fields):
-            indSMBar = self.fields.index('SMBautoregression')  # Index of SMBar, now check for consistency with other timesteps
-            dimensions[indSMBar] = md.smb.num_basins
-            if(md.smb.ar_timestep<self.stochastictimestep):
-                raise TypeError('SMBautoregression cannot have a timestep shorter than stochastictimestep')
-        if ('FrontalForcingsRignotAutoregression' in self.fields):
-            indTFar = self.fields.index('FrontalForcingsRignotAutoregression')  # Index of TFar, now check for consistency with other timesteps
-            dimensions[indTFar] = md.frontalforcings.num_basins
-            if(md.frontalforcings.ar_timestep<self.stochastictimestep):
-                raise TypeError('FrontalForcingsRignotAutoregression cannot have a timestep shorter than stochastictimestep')
-        if ('BasalforcingsDeepwaterMeltingRateAutoregression' in self.fields):
-            indBDWar = self.fields.index('BasalforcingsDeepwaterMeltingRateAutoregression')  # Index of BDWar, now check for consistency with other timesteps
-            dimensions[indTFar] = md.basalforcings.num_basins
-            if(md.basalforcings.ar_timestep<self.stochastictimestep):
-                raise TypeError('BasalforcingsDeepwaterMeltingRateAutoregression cannot have a timestep shorter than stochastictimestep')
+        indSMBarma   = -1  # About to check for index of SMBarma
+        indTFarma    = -1  # About to check for index of FrontalForcingsRignotarma
+        indBDWarma   = -1  # About to check for index of BasalforcingsDeepwaterMeltingRatearma
+        if ('SMBarma' in self.fields):
+            indSMBarma = self.fields.index('SMBarma')  # Index of SMBarma, now check for consistency with other timesteps
+            dimensions[indSMBarma] = md.smb.num_basins
+            if(md.smb.arma_timestep<self.stochastictimestep):
+                raise TypeError('SMBarma cannot have a timestep shorter than stochastictimestep')
+        if ('FrontalForcingsRignotarma' in self.fields):
+            indTFarma = self.fields.index('FrontalForcingsRignotarma')  # Index of TFarma, now check for consistency with other timesteps
+            dimensions[indTFarma] = md.frontalforcings.num_basins
+            if(md.frontalforcings.arma_timestep<self.stochastictimestep):
+                raise TypeError('FrontalForcingsRignotarma cannot have a timestep shorter than stochastictimestep')
+        if ('BasalforcingsDeepwaterMeltingRatearma' in self.fields):
+            indBDWarma = self.fields.index('BasalforcingsDeepwaterMeltingRatearma')  # Index of BDWarma, now check for consistency with other timesteps
+            dimensions[indTFarma] = md.basalforcings.num_basins
+            if(md.basalforcings.arma_timestep<self.stochastictimestep):
+                raise TypeError('BasalforcingsDeepwaterMeltingRatearma cannot have a timestep shorter than stochastictimestep')
         size_tot = np.sum(dimensions)
 
-        if (indSMBar != -1 and indTFar != -1):  # Both autoregressive models are used: check autoregressive time step consistency
-            covsum = self.covariance[np.sum(dimensions[0:indSMBar]).astype(int):np.sum(dimensions[0:indSMBar + 1]).astype(int), np.sum(dimensions[0:indTFar]).astype(int):np.sum(dimensions[0:indTFar + 1]).astype(int)]
-            if((md.smb.ar_timestep != md.frontalforcings.ar_timestep) and np.any(covsum != 0)):
-                raise IOError('SMBautoregression and FrontalForcingsRignotAutoregression have different ar_timestep and non-zero covariance')
-        if (indSMBar != -1 and indBDWar != -1):  # Both autoregressive models are used: check autoregressive time step consistency
-            covsum = self.covariance[np.sum(dimensions[0:indSMBar]).astype(int):np.sum(dimensions[0:indSMBar + 1]).astype(int), np.sum(dimensions[0:indBDWar]).astype(int):np.sum(dimensions[0:indBDWar + 1]).astype(int)]
-            if((md.smb.ar_timestep != md.basalforcings.ar_timestep) and np.any(covsum != 0)):
-                raise IOError('SMBautoregression and BasalforcingsDeepwaterMeltingRateAutoregression have different ar_timestep and non-zero covariance')
-        if (indTFar != -1 and indBDWar != -1):  # Both autoregressive models are used: check autoregressive time step consistency
-            covsum = self.covariance[np.sum(dimensions[0:indTFar]).astype(int):np.sum(dimensions[0:indTFar + 1]).astype(int), np.sum(dimensions[0:indBDWar]).astype(int):np.sum(dimensions[0:indBDWar + 1]).astype(int)]
-            if((md.frontalforcings.ar_timestep != md.basalforcings.ar_timestep) and np.any(covsum != 0)):
-                raise IOError('FrontalForcingsRignotAutoregression and BasalforcingsDeepwaterMeltingRateAutoregression have different ar_timestep and non-zero covariance')
+        if (indSMBarma != -1 and indTFarma != -1):  # Both ARMA models are used: check ARMA time step consistency
+            covsum = self.covariance[np.sum(dimensions[0:indSMBarma]).astype(int):np.sum(dimensions[0:indSMBarma + 1]).astype(int), np.sum(dimensions[0:indTFarma]).astype(int):np.sum(dimensions[0:indTFarma + 1]).astype(int)]
+            if((md.smb.arma_timestep != md.frontalforcings.arma_timestep) and np.any(covsum != 0)):
+                raise IOError('SMBarma and FrontalForcingsRignotarma have different arma_timestep and non-zero covariance')
+        if (indSMBarma != -1 and indBDWarma != -1):  # Both ARMA models are used: check ARMA time step consistency
+            covsum = self.covariance[np.sum(dimensions[0:indSMBarma]).astype(int):np.sum(dimensions[0:indSMBarma + 1]).astype(int), np.sum(dimensions[0:indBDWarma]).astype(int):np.sum(dimensions[0:indBDWarma + 1]).astype(int)]
+            if((md.smb.arma_timestep != md.basalforcings.arma_timestep) and np.any(covsum != 0)):
+                raise IOError('SMBarma and BasalforcingsDeepwaterMeltingRatearma have different arma_timestep and non-zero covariance')
+        if (indTFarma != -1 and indBDWarma != -1):  # Both ARMA models are used: check ARMA time step consistency
+            covsum = self.covariance[np.sum(dimensions[0:indTFarma]).astype(int):np.sum(dimensions[0:indTFarma + 1]).astype(int), np.sum(dimensions[0:indBDWarma]).astype(int):np.sum(dimensions[0:indBDWarma + 1]).astype(int)]
+            if((md.frontalforcings.arma_timestep != md.basalforcings.arma_timestep) and np.any(covsum != 0)):
+                raise IOError('FrontalForcingsRignotarma and BasalforcingsDeepwaterMeltingRatearma have different arma_timestep and non-zero covariance')
 
         md = checkfield(md, 'fieldname', 'stochasticforcing.isstochasticforcing', 'values', [0, 1])
@@ -184,13 +184,13 @@
             for ind, field in enumerate(self.fields):
                 # Checking for specific dimensions
-                if (field == 'SMBautoregression'):
+                if (field == 'SMBarma'):
                     dimensions[ind] = md.smb.num_basins
-                if (field == 'FrontalForcingsRignotAutoregression'):
+                if (field == 'FrontalForcingsRignotarma'):
                     dimensions[ind] = md.frontalforcings.num_basins
-                if (field == 'BasalforcingsDeepwaterMeltingRateAutoregression'):
+                if (field == 'BasalforcingsDeepwaterMeltingRatearma'):
                     dimensions[ind] = md.basalforcings.num_basins
 
             # Scaling covariance matrix (scale column-by-column and row-by-row)
-            scaledfields = ['BasalforcingsDeepwaterMeltingRateAutoregression','BasalforcingsSpatialDeepwaterMeltingRate','DefaultCalving', 'FloatingMeltRate', 'SMBautoregression', 'SMBforcing']  # list of fields that need scaling * 1/yts
+            scaledfields = ['BasalforcingsDeepwaterMeltingRatearma','BasalforcingsSpatialDeepwaterMeltingRate','DefaultCalving', 'FloatingMeltRate', 'SMBarma', 'SMBforcing']  # list of fields that need scaling * 1/yts
             tempcovariance = np.copy(self.covariance)
             for i in range(num_fields):
@@ -229,5 +229,5 @@
            supported and corresponding md names
         """
-        structure = {'BasalforcingsDeepwaterMeltingRateAutoregression': 'autoregressionlinearbasalforcings',
+        structure = {'BasalforcingsDeepwaterMeltingRatearma': 'linearbasalforcingsarma',
                      'BasalforcingsSpatialDeepwaterMeltingRate': 'spatiallinearbasalforcings',
                      'DefaultCalving': 'calving',
@@ -236,6 +236,6 @@
                      'FrictionCoulombWaterPressure': 'frictioncoulomb',
                      'FrictionSchoofWaterPressure': 'frictionschoof',
-                     'FrontalForcingsRignotAutoregression': 'frontalforcingsrignotautoregression',
-                     'SMBautoregression': 'SMBautoregression',
+                     'FrontalForcingsRignotarma': 'frontalforcingsrignotarma',
+                     'SMBarma': 'SMBarma',
                      'SMBforcing': 'SMBforcing'}
         return structure