## Projects

# Uncertainty Quantification for ECOSTRESS

ECOSTRESS is NASA's ECOsystem Spaceborne Thermal Radiometer Experiment on
Space Station. It is designed to retrieve evapotranspiration heat flux
using spectroscopy in the thermal infrared.
Uncertainty quantification for ECOSTRESS evapotranspiration extends the
paradigm of *simulation-based uncertainty quantification* by
simulating whole spatial fields at one time. In the original, non-spatial
version of simulation-based uncertainty quantification (used for OCO-2 and
AIRS), we created ensembles by drawing synthetic true state vectors from a
distribution of potential states, one-at-a-time and independently. Here, we
generate ensemble of spatial fields by simulating them from a fitted
spatial statistical model. The model is fit to a parent exemplar field
(such as the output of deterministic model). Then, we apply the ECOSTRESS
retrieval to all pixels in all ensemble members. Each pixel has a stack of
synthetic true ET values and corresponding stack of synthetic retrieved ET
values, which are used to produce a stack of ET retrieval errors. These
distributions can be used to characterize ET retrieval performance in each
pixel individually, but this strategy allows us to do more: we can
investigate the covariance properties of the errors. This is valuable for
quantifying uncertainties in estimated spatial gradients using ECOSTRESS
data products.

Median ET error, by pixel, for a scene in Nebraska, USA. | Distributions (over all pixels and all ensemble members) of retrieved ET error (ET minus synthetic true ET) for six scenes (A-G) in CONUS. |

## Selected Publications

Cawse-Nicholson, K., Braverman, A., Kang, Emily L., Li, M., Johnson, M., Halverson, G., Anderson, M., Haind, C., Gunson, M., and and Hook, S. (2020). “Sensitivity and uncertainty quantification for the ECOSTRESS
evapotranspiration algorithm – DisALEXI,” *International Journal on Applied Earth Observations and Geoinformation*. Download.

Ma, P. and Kang, E.L. (2020). “A Fused Gaussian Process Model for Very Large Spatial Data,” *Journal of Computational and Graphical Statistics*, pp. 1-11. Download.

Ma, P., Kang, E.L., Braverman, A.J., and and Nguyen, H.M. (2019). “Spatial Statistical Downscaling for Constructing High-resolution Nature Runs in Global Observing System Simulation Experiments,” *Technometrics*, **61**(3), pp. 322-340. Download.