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Data Assimilation (DA) Input Preprocessing

What LIS DA observation dataset types are supported in LDT and LIS?

The current public LIS versions support the following data assimilation (DA) observation dataset types:
  • Soil Moisture Active Passive (SMAP) soil moisture products
  • NASA/Vrije U. Land Parameter Retrieval Model (LPRM) AMSR-e Soil Moisture data
  • NASA/NSIDC AMSR-e Soil Moisture data
  • Essential Climate Variable (ECV) Satellite-based Soil Moisture analysis
  • TU Wien ASCAT Soil Moisture (retrospective)
  • NESDIS/OSPO Soil Moisture Operational Products System (SMOPS) Soil Moisture product

What is CDF matching and where can I learn more about it?

One method for reducing systematic biases between the observational data to be assimilated and the model's state estimates to be updated with that data is known as "CDF matching". The cumulative density function, or aka the "CDF", characterizes the cumulative probability of a random variable (say, X) up to a specific point that is equal to the desired area under the associated PDF curve, for example left of that specific point.

What type of data assimilation inputs does LDT process?

Currently, LDT can estimate the statistics required to do a simple bias-correction or scaling approach between similar observational data and model state estimates to reduce bias between the two during assimilation update step. LDT generates the mean, standard deviation and cumulative (probability) density function (CDF) values, which LIS-7 ingests to perform the final CDF "matching" between the observations and the model estimates.