diff --git a/experiments/india/008_coarse4/readme.md b/experiments/india/008_coarse4/readme.md
index b3915361..404d0a91 100644
--- a/experiments/india/008_coarse4/readme.md
+++ b/experiments/india/008_coarse4/readme.md
@@ -1,6 +1,6 @@
 # Coarser data and more examples
 
-We down samples the ECMWF data from 0.05 to 0.2. 
+We down samples the ECMWF data from 0.05 to 0.2.
 In previous experiments we used a 0.1 resolution, as this is the same as the live ECMWF data.
 
 By reducing the resolution we can increase the number of samples we have to train on.
@@ -11,12 +11,12 @@ This is approximately 5 times more samples than the previous experiments.
 
 
 ### b8_s1
-Batche size 8, with 0.2 degree NWP data. 
+Batche size 8, with 0.2 degree NWP data.
 https://wandb.ai/openclimatefix/india/runs/w85hftb6
 
 
 ### b8_s2
-Batch size 8, different seed, with 0.2 degree NWP data. 
+Batch size 8, different seed, with 0.2 degree NWP data.
 https://wandb.ai/openclimatefix/india/runs/k4x1tunj
 
 ### b32_s3
@@ -38,18 +38,20 @@ https://wandb.ai/openclimatefix/india/runs/a5nkkzj6
 
 
 ### old
-Old experiment with 0.1 degree NWP data. 
+Old experiment with 0.1 degree NWP data.
 https://wandb.ai/openclimatefix/india/runs/m46wdrr7.
 Note the validation batches are different that the experiments above.
 
-Interesting the GPU memory did not increase much better experiments 2 and 3. 
-Need to check that 32 batches were being passed through. 
+Interesting the GPU memory did not increase much better experiments 2 and 3.
+Need to check that 32 batches were being passed through.
 
 ## Results
 
 The coarsening data does seem to improve the experiments results in the first 10 hours of the forecast.
 DA forecast looks very similar. Note the 0 hour forecast has a large amount of variation. 
 
+
+
 Still spike results in the individual runs. 
 
 | Timestep | b8_s1 MAE % | b8_s2 MAE % | b32_s3 MAE % | epochs MAE % | small MAE % | mae/val MAE % | old MAE % |
@@ -72,4 +74,5 @@ Still spike results in the individual runs.
 ![](mae_step_smooth.png "mae_steps")
 
 I think its worth noting the model traing MAE is around `3`% and the validation MAE is about `7`%, so there is good reason to believe that the model is over fit to the trianing set. 
-It would be good to plot some of the trainin examples, to see if they are less spiky. 
\ No newline at end of file
+It would be good to plot some of the trainin examples, to see if they are less spiky. 
+