Algorithms for atmospheric water balance method

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SUMMARY

The discussion focuses on estimating the divergence of water vapor flux in the atmosphere using NCEP/NCAR data. The participant seeks algorithms to implement this estimation, referencing existing algorithms designed for ECMWF data. Key considerations include potential differences in data formatting and grid size between NCEP/NCAR and ECMWF datasets, as well as the need to address biases in cloud variables that may affect water vapor measurements.

PREREQUISITES
  • Understanding of atmospheric water balance concepts
  • Familiarity with NCEP/NCAR and ECMWF datasets
  • Knowledge of data formatting and grid size implications
  • Experience with algorithm implementation in atmospheric sciences
NEXT STEPS
  • Research the differences between NCEP/NCAR and ECMWF datasets
  • Explore data formatting requirements for atmospheric data analysis
  • Investigate algorithms specifically designed for NCEP/NCAR data
  • Study the impact of biases in cloud variables on water vapor measurements
USEFUL FOR

Atmospheric scientists, researchers in meteorology, and data analysts focused on water vapor flux estimation and atmospheric modeling.

ablaye
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Hi
I want to estimate divergence of water vapor flux in atmosphere with NCEP/NCAR data
I have the equations for that and i want the algorithms to implement it for NCEP/NCAR data.

I have a paper with algorithms for ECMWF data. Can i take this? or it required new algorithms for my data
 
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Can you describe the differences between the data sets?

You may need to change things related to data formatting or grid size.

I know that ERA has biases in its cloud variables so you may want to be aware if there are any for water vapor that are corrected since ECMWF is more like model data.
 

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