[Rda-users] NCAR Research Data Archive Blog

[NCAR Research Data Archive Blog]

NCAR Research Data Archive Blog

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The data starts here

Posted: 18 May 2015 03:30 PM PDT
http://feedproxy.google.com/~r/NcarResearchDataArchiveBlog/~3/HXcWUvyhvrw/the-data-starts-here.html?utm_source=feedburner&utm_medium=email

At the Water Conservation Gardens Fair yesterday in Berthoud, Colorado  
(near NCAR's Boulder home), several vendors showed soil moisture monitoring  
systems.  I took a picture to show where data comes from.

The green-blue rectangle displayed at the top is the soil moisture sensor,  
which is buried in the ground.
Soil moisture monitor/data logger and one sensor.An irregularly-spaced  
network of calibrated soil moisture sensors (not necessarily this brand and  
model) send their data to National Center for Environmental Prediction  
(NCEP) in College Park, Maryland. Other sensors, some in-situ like this  
one, others remotely sensed (usually from satellites), also send their data  
to NCEP.

The data is ingested into an analysis model to produce a gridded analysis  
field.

Some of our data users may not be aware that NCEP and NCAR are different  
organizations.  NCEP is part of the National Oceanic and Atmospheric  
Administration (NOAA), which is part of the Department of Commerce (DoC).   
DoC headquarters are in Maryland, but they have a significant laboratory in  
Boulder, Colorado, home of NCAR.

Notice that we are several thousand miles and two timezones apart.
NCEP and NCAR are quite far apart in distance, but the internet brings us  
closer.This is where NCEP processes the data.
NOAA NCEP Center for Weather and Climate Prediction, College Park, Md
This is where we pull and archive data from NCEP and other sources.
NCAR Mesa Lab, home of NCAR RDAInterested in learning about how data gets  
calibrated?  Leave a comment with your name, organization or school, and  
what you'd like to read about on this blog.

Today, I took a bike ride to collect qualitative (not quantitative) data  
about soil moisture
and stream flow.  ;-)
The RDA's most popular dataset is FNL, ds083.2.  It's popularity largely  
stems from its utility for initializing The Weather Research & Forecasting  
Model (WRF).  Studies have proven that improved soil moisture estimates  
provide more accurate rainfall predictions.

If you use (or want to learn how to use) FNL for WRF, please read the help  
document, Guidance for WRF users for new NCEP GFS and FNL GRIB2 files.   
Notice that the Vtable (variable table) includes several soil moisture  
parameters at different depth.

Accurate soil moisture, soil type and land-use characterization help  
produce accurate numerical weather forecasts.  It takes a huge amount of  
data, many people, much software, and vast computational resources to  
produce these analyses.  So, when you use the data, take a minute to think  
about the thousands of people who made it possible.

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