This is the first plot:

I completed the first run for prediction for year 2010 for the 6 tiles in Venezuela. I spent some time to check the results and revised the RMSE
presented during the IPLANT meeting.The code is currently working for GAM fusion with 9 models:
Mod1: y_var ~ s(elev_1)
Mod2: y_var ~ s(LST)
Mod3: y_var ~ s(elev_1 LST)
Mod4: y_var ~ s(lat) + s(lon)+ s(elev_1)
Mod5: y_var ~ s(lat,lon,elev_1)
Mod6: y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) ,
Mod7: y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC2)
Mod8: y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC6)
Mod9: y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(DISTOC)
Mod_kr: kriging

Here are the first results for January 3, 2010:

Figure 1 shows the relation between TMax and LST.
Note the low bias of -0.02 for January compared to -2.54C in Oregon.There are 52 observations available for the calculation of the bias surface.
Figure 2 displays the relation between dailyTmax and monthly TMax.
We have 31 training stations but only 18 with actual observations. Note that 13 stations were kept for the validation.
Figure 3 compares predicted and observed dailyTmax for Venezuela.

Figure 4 displays the interpolated maps for 5 models. There are no predictions for Model 5 to model 9.
This is because there are not enough observations to fit GAM models especially when there are interactive smooth terms.
RMSEs for each model are displayed on the top of the maps.DailyTmax show variations (from warm to cooler) in grass, forest and mountains areas.

I have now put together a short script that will be turned into a function to generate some useful output based on the raster prediction per date (see 03aa4873). Here are a few of the generated figures.

Study area

I have now put together a short script that will be turned into a function to generate some useful output based on the raster prediction per date (see 03aa4873). Here are a few of the generated figures.

Fig1:Study area Venezuela

This figure is generated using land cover 12 (LC12: water) from the consensus product. There are some disagreeements in terms of how many pixels are valid among different inputs (LST, ELEV_SRTM etc.). According to LC12 mask There are 3,684,606 pixels that are land out of a total of 8,640,000. This means that about 42.65% of the image.

Fig2: Map of monthly ghcn stations

This is a map of the 52 ghcnd station with monthly TMAX averages. Thsi is for the month of January. The S flag was added to obtain more stations. The average is done over the 2000-2010 time period. I'll be doing the same thing using the 1980-2010 time period to get more data point for the GAM fitting.

Fig3: Map of training and testing stations-20100103

Testing (validation) stations are in red and training in black(triangles).

Fig4: Scatterplot tmax predicted and observed-20100103

This plot shows the predicted tmax for both testing and training sites. The RMSE is 2.08 for for GAM using kriging without covariates. Note that out of 31 stations, only 18 have valid TMAX daily information.

Fig5: Map of bias and delta surfaces for-20100103

Fusion method: Bias surfaces are for January. There are 5 bias surfaces (see models used in previous update) predicted using GAM and covariates (with the exception of mod_kr using kriging). The delta surface is obtained through kriging.

Fig6: Map of bias and delta surfaces for-20100103

Fusion method: predicted tmax surfaces with RMSE. This is a repeat from last update. There are 5 predicted surfaces (see models used in previous update) for January 3, 2010.

Fig7: Daily tmax RMSE boxplot for year 2010

This is an output of the validation script(dc5bfc17). RMSEs are the lowest for model 3 (mean 2.04C)and mod_kr (mean 2.07C)last boxplot on the right). Model 3 includes LST and Elevation (s(LST,elev_1).

Fig8: Daily tmax MAE boxplot for year 2010

This is also an output of the validation script(dc5bfc17) showing the Mean Absolute Error (MAE) for the validation stations over 365 dates (year 2010). Results are similar to RMSEs i.e. mod3 and mod_kr have the lowest MAE with mean values of 1.56C and 1.58C respectively.

Mean and median values for validation metrics for year 2010

Here is the output for the mean and median values derived from the daily prediction of tmax for the Venezuela region.