<p>Some variables in reanalyses have to be forecast and cannot be assimilated to allow the model to converse mass, momentum and energy. Precipitation is a good example. The model will assimilate observations which give information about moisture (someone who knows more about assimilation may want to chirp in here). This will be used in the analysis (e.g. 00z) and is forecast forward. Daily precipitation is therefore given as the accumulation of precipitation from 00z analysis -> 12z forecast and the 12z analysis -> 24z forecast.</p>

<p>There is a hierarchy of precipitation datasets each with pros and cons:</p>

<ul>
<li>Gauge data is often considered the best but it very patchy (not to mentions instrument issues). You can use <a href="https://en.wikipedia.org/wiki/Kriging" rel="nofollow noreferrer">kriging</a> to make spatial maps. This <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2017-508/" rel="nofollow noreferrer">paper</a> may be of interest.</li>
<li>Satellite datasets such as <a href="https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html" rel="nofollow noreferrer">GPCP</a> and <a href="https://pmm.nasa.gov/trmm" rel="nofollow noreferrer">TRMM</a> give better spatial information but subject to issues over land, for example (I think). The are also subject to trackiness (only get data underneath/near the satellite).</li>
<li>Reanalyses can provide spatial and temporal homogeneity but it is a model and 'all models are wrong, but some are useful'.  </li>
<li>Datasets are emerging combining both gauge, satellite and reanalyses such as in this <a href="https://www.hydrol-earth-syst-sci.net/21/589/2017/" rel="nofollow noreferrer">paper</a>.</li>
</ul>

CC BY-SA 3.0
2017-08-31T20:12:31.427Z
So this means that the daily accumulated precipitation is entirely given by the model, which receives daily "initial condition" analysis variables, such as moisture. Therefore the amount of rainfall is very dependent on model parameterizations and so on. Am I getting it right?
ouranos
CC BY-SA 3.0
2017-08-31T20:13:59.313Z
I intend to use NCEP's daily precipitation rate because it is one of the only sources of daily precipitation from mid 1800's. Another option is ERA 20C (1900 to present). Any other suggestions would be very welcome.
ouranos
CC BY-SA 3.0
2017-08-31T20:27:50.023Z
Yes. You are right how precip is sensitive to the model's physics package etc..

I think they are your only bets if you are interested that far back. A good approach might be to use both and compare their climatology to climatology from more recent reanalyses. There are some online tools you can use as a first order check https://www.esrl.noaa.gov/psd/cgi-bin/data/testdap/plot.comp.pl
Might give you some indication on which one is 'better' to use for your application.
Ray Bell
CC BY-SA 3.0
2017-08-30T18:17:29.763Z
2017-08-30T18:17:29.763Z
Ray Bell
<p>NCEP's Twentieth Century Reanalysis (V2) provides daily variables from 1871/01/01 to 2012/12/31. Monolayer <strong>analysis variables</strong> are made available in a regular grid and <strong>forecast variables</strong> in a gaussian grid. This distinction is made very clear in their list on <a href="https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.monolevel.html" rel="nofollow noreferrer">their webpage</a>, although I think the grid is not the main problem here, but the terms in bold.</p>

<p>Daily precipitation rate, for example, is a forecast variable. This makes you think weather you may use forecast and analysis interchangeably. What are the main differences? Any pros or cons? Are all of these still considered <strong>reanalysis</strong>? Any assumputions I need to make?</p>

<p>This question came up as a consequence of the discussion held <a href="https://earthscience.stackexchange.com/questions/10727/single-layer-water-content-variables">here</a>.</p>

<p>Thank you in advance.</p>

<meteorology><climate-models><open-data><climatology><reanalysis>
Difference between NCEP's 20th Century daily Forecast and Analysis variables
ouranos
# Difference between NCEP's 20th Century daily Forecast and Analysis variables

(Asked by: username_0 on 2017-08-30)

NCEP's Twentieth Century Reanalysis (V2) provides daily variables from 1871/01/01 to 2012/12/31. Monolayer **analysis variables** are made available in a regular grid and **forecast variables** in a gaussian grid. This distinction is made very clear in their list on [their webpage](https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2.monolevel.html), although I think the grid is not the main problem here, but the terms in bold.

Daily precipitation rate, for example, is a forecast variable. This makes you think weather you may use forecast and analysis interchangeably. What are the main differences? Any pros or cons? Are all of these still considered **reanalysis**? Any assumputions I need to make?

This question came up as a consequence of the discussion held [here](https://earthscience.stackexchange.com/questions/10727/single-layer-water-content-variables).

Thank you in advance.

**Tags:** meteorology, climate-models, open-data, climatology, reanalysis


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## 1 answers

### Answer by username_1 on 2017-08-30. Score: 3 | Accepted answer

Some variables in reanalyses have to be forecast and cannot be assimilated to allow the model to converse mass, momentum and energy. Precipitation is a good example. The model will assimilate observations which give information about moisture (someone who knows more about assimilation may want to chirp in here). This will be used in the analysis (e.g. 00z) and is forecast forward. Daily precipitation is therefore given as the accumulation of precipitation from 00z analysis -> 12z forecast and the 12z analysis -> 24z forecast.

There is a hierarchy of precipitation datasets each with pros and cons:

* Gauge data is often considered the best but it very patchy (not to mentions instrument issues). You can use [kriging](https://en.wikipedia.org/wiki/Kriging) to make spatial maps. This [paper](https://www.hydrol-earth-syst-sci-discuss.net/hess-2017-508/) may be of interest.
* Satellite datasets such as [GPCP](https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html) and [TRMM](https://pmm.nasa.gov/trmm) give better spatial information but subject to issues over land, for example (I think). The are also subject to trackiness (only get data underneath/near the satellite).
* Reanalyses can provide spatial and temporal homogeneity but it is a model and 'all models are wrong, but some are useful'.
* Datasets are emerging combining both gauge, satellite and reanalyses such as in this [paper](https://www.hydrol-earth-syst-sci.net/21/589/2017/).

> **Comments:**
> **username_0**: So this means that the daily accumulated precipitation is entirely given by the model, which receives daily "initial condition" analysis variables, such as moisture. Therefore the amount of rainfall is very dependent on model parameterizations and so on. Am I getting it right?  
> **username_0**: I intend to use NCEP's daily precipitation rate because it is one of the only sources of daily precipitation from mid 1800's. Another option is ERA 20C (1900 to present). Any other suggestions would be very welcome.  
> **username_1**: Yes. You are right how precip is sensitive to the model's physics package etc..  I think they are your only bets if you are interested that far back. A good approach might be to use both and compare their climatology to climatology from more recent reanalyses. There are some online tools you can use as a first order check https://www.esrl.noaa.gov/psd/cgi-bin/data/testdap/plot.comp.pl Might give you some indication on which one is 'better' to use for your application.  

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