| Label | Explanation | Data Type |
Input Background Raster
| The input background raster also known as the background field. | Raster Dataset; Raster Layer; Image Service |
Input Observation Data | The input point features that will be used for interpolation. | Feature Layer; Trajectory Layer |
Observation Field
| The field containing observation values that will be used for interpolation. | String |
Output Raster
| The output multidimensional raster dataset. | Raster Dataset |
Background Error Variance
| The error variance of the background measurements. The input can be a single value or an error variance raster. If a single value is provided, the value will be used as the error variance for all background measurements. If an error variance raster is provided, each cell in the background data will obtain its error variance from the corresponding background error variance raster. The error variance raster must have the same cell size and extent as the background data. | Double; Raster Dataset; Raster Layer; Image Service |
Observation Error Variance | The error variance of the observations. The input can be a single value or a field from the observation data. If a single value is provided, the value will be used as the error variance for all observations. If a field in the observation data is provided, values in the field will be used as the error variance for each corresponding observation point. | Double; String |
Background Error Correlation Length
(Optional) | The correlation length between background measurements. The default is three times the cell size of the Input Background Raster parameter value. | Double |
Available with Image Analyst license.
Summary
Statistically assimilates data combined from multiple sources to produce an output raster. The tool can be used to merge background data, such as model outputs, with observation data, such as point measurements, to perform interpolation.
Usage
The tool accepts a background data set and an observation data set as input. The Input Background Raster parameter value is typically a gridded raster that may come from a model output, and the Input Observation Data parameter value is typically a feature or trajectory data set that contains observations in the form of points.
To combine the data sets, weights are assigned to the background and observation data sets based on their relative accuracies to minimize analysis error variance. The relative accuracies are determined from the Background Error Variance, Observation Error Variance, and Background Error Correlation Length parameter values, which are used to determine the background error correlation between background measurements.
The Background Error Variance and Observation Error Variance parameter values are typically global constants estimated from the background and observation data. Alternatively, you can provide another raster with the same rows and columns, cell size, and extent to represent the background error variance. You can also provide a field from the observation data that represents the observation error variance.
The Background Error Correlation Length parameter value is used to estimate the influence of an observation point on the output. It is expressed in the unit of the spatial reference of the input background data. A higher value gives a higher influence on points farther away from the output cell.
Observation error correlation is assumed to be 0.
Parameters
OptimalInterpolation(in_background_raster, in_obs_data, obs_field, out_raster, background_error_var, obs_error_var, {background_error_corr_length})| Name | Explanation | Data Type |
in_background_raster | The input background raster also known as the background field. | Raster Dataset; Raster Layer; Image Service |
in_obs_data | The input point features that will be used for interpolation. | Feature Layer; Trajectory Layer |
obs_field | The field containing observation values that will be used for interpolation. | String |
out_raster | The output multidimensional raster dataset. | Raster Dataset |
background_error_var | The error variance of the background measurements. The input can be a single value or an error variance raster. If a single value is provided, the value will be used as the error variance for all background measurements. If an error variance raster is provided, each cell in the background data will obtain its error variance from the corresponding background error variance raster. The error variance raster must have the same cell size and extent as the background data. | Double; Raster Dataset; Raster Layer; Image Service |
obs_error_var | The error variance of the observations. The input can be a single value or a field from the observation data. If a single value is provided, the value will be used as the error variance for all observations. If a field in the observation data is provided, values in the field will be used as the error variance for each corresponding observation point. | Double; String |
background_error_corr_length (Optional) | The correlation length between background measurements. The default is three times the cell size of the in_background_raster parameter value. | Double |
Code sample
This example interpolates elevation point data into a multidimensional raster.
# Import system modules
import arcpy from arcpy import *
#Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
# Set local variables
in_background_raster = r"C:\data\global_ssh.tif"
in_obs_data = r"C:\data\jason_3_ssh.shp"
obs_field = "SSH"
background_error_var = 0.1
obs_error_var = 0.2
background_error_corr_length = 100 # km
# Execute
output = arcpy.ia.OptimalInterpolation(in_background_raster, in_obs_data, obs_field, background_error_var, obs_error_var, background_error_corr_length)
Output.save(r”c:\output\OI_ssh.crf”)Environments
Licensing information
- Basic: Requires Image Analyst
- Standard: Requires Image Analyst
- Advanced: Requires Image Analyst