diff --git a/5. Data Assimilation/Utilities/Ensrf/jointENSRF.m b/5. Data Assimilation/Utilities/Ensrf/jointENSRF.m
index 95ba6830..c833dd99 100644
--- a/5. Data Assimilation/Utilities/Ensrf/jointENSRF.m	
+++ b/5. Data Assimilation/Utilities/Ensrf/jointENSRF.m	
@@ -42,26 +42,30 @@
 % For each time step
 parfor t = 1:nTime
     
+    % Slice variables
+    Rt = R(:,t);
+    Dt = D(:,t);
+    
     % Update using obs that are not NaN
-    update(:,t) = update(:,t) & ~isnan( D(:,t) );
+    update(:,t) = update(:,t) & ~isnan( Dt );
     obs = update(:,t);
     
     % Get the full kalman gain and kalman denominator
-    [K, Kdenom] = jointKalman( 'K', Knum(:,obs), Ydev(obs,:), yloc(obs,obs), R(obs,t) ); %#ok<PFBNS>
+    [K, Kdenom] = jointKalman( 'K', Knum(:,obs), Ydev(obs,:), yloc(obs,obs), Rt(obs) ); %#ok<PFBNS>
     
     % Update the mean
-    Amean(:,t) = Mmean + K * ( D(obs,t) - Ymean(obs) ); %#ok<PFBNS>
+    Amean(:,t) = Mmean + K * ( Dt(obs) - Ymean(obs) ); %#ok<PFBNS>
     K = [];   %#ok<NASGU>  (Free up memory)
 
     % If returning variance
     if ~meanOnly
     
         % Get the adjusted kalman gain
-        Ka = jointKalman( 'Ka', Knum(:,obs), Kdenom, R(obs,t) );
+        Ka = jointKalman( 'Ka', Knum(:,obs), Kdenom, Rt(obs) );
 
         % Update the deviations and get the variance
         Avar(:,t) = var(   Mdev - Ka * Ydev(obs,:),   0, 2 );
     end
 end
 
-end
+end
\ No newline at end of file
diff --git a/5. Data Assimilation/Utilities/Ensrf/serialENSRF.m b/5. Data Assimilation/Utilities/Ensrf/serialENSRF.m
index c6dbb9f6..954d0f68 100644
--- a/5. Data Assimilation/Utilities/Ensrf/serialENSRF.m	
+++ b/5. Data Assimilation/Utilities/Ensrf/serialENSRF.m	
@@ -1,4 +1,4 @@
-function[Amean, Avar, Ye, update] = serialENSRF( M, D, R, F, w )
+function[Amean, Avar, Ye, R, update] = serialENSRF( M, D, R, F, w )
 %% Implements data assimilation using an ensemble square root filter with serial
 % updates for individual observations. Time steps are assumed independent
 % and processed in parallel.
@@ -62,7 +62,7 @@
             Mpsm = Am(F{d}.H) + Ad(F{d}.H,:);                   %#ok<PFBNS>
             
             % Run the PSM. Generate R. Error check.
-            [Ye(d,:,t), R(d,t), update(d,t)] = getPSMOutput( F{d}, Mpsm, R(d,t), t, d ); %#ok<PFOUS>
+            [Ye(d,:,t), R(d,t), update(d,t)] = getPSMOutput( F{d}, Mpsm, R(d,t), t, d );
             
             % If no errors occured in the PSM, and the R value is valid,
             % update the analysis
@@ -72,7 +72,7 @@
                 [Ymean, Ydev] = decomposeEnsemble( Ye(d,:,t) );
 
                 % Get the Kalman gain and alpha scaling factor
-                [K, a] = serialKalman( Ad, Ydev, w(:,d), 1, R(d,t) );   %#ok<PFBNS>
+                [K, a] = serialKalman( Ad, Ydev, w(:,d), 1, R(d,t) );    %#ok<PFBNS>
 
                 % Update
                 Am = Am + K*( D(d,t) - Ymean );