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4 changes: 2 additions & 2 deletions docs/Contributors_Guide/index.rst
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===================
###################
Contributor's Guide
===================
###################

Welcome to the Model Evaluation Tools (MET) Contributor's Guide.

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18 changes: 9 additions & 9 deletions docs/Users_Guide/appendixA.rst
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Expand Up @@ -1816,7 +1816,7 @@ scripts available in the MET's *scripts/* directory show examples of how
one might use these commands on example datasets. Here are suggestions
on other things to check if you are having problems installing or running MET.

MET won't compile
MET Won't Compile
-----------------

.. dropdown:: Troubleshooting Help
Expand All @@ -1828,7 +1828,7 @@ MET won't compile
* Have these libraries been compiled and installed using the same set
of compilers used to build MET?

BUFRLIB Errors during MET installation
BUFRLIB Errors During MET Installation
--------------------------------------

.. dropdown:: Troubleshooting Help
Expand Down Expand Up @@ -1866,7 +1866,7 @@ BUFRLIB Errors during MET installation
`METplus GitHub Discussions Forum <https://github.com/dtcenter/METplus/discussions>`_.


Command line double quotes
Command Line Double Quotes
--------------------------

.. dropdown:: Troubleshooting Help
Expand All @@ -1882,7 +1882,7 @@ Command line double quotes
'G003', '/h/data/global/WXQC/data/met/nc_mdl/umm/1701150006', '- field',
'\'name="HGT"; level="P500";\'', '-v', '6']

Environment variable settings
Environment Variable Settings
-----------------------------

.. dropdown:: Troubleshooting Help
Expand Down Expand Up @@ -1916,7 +1916,7 @@ Environment variable settings
value should be:
https://met.readthedocs.io/en/latest/Users_Guide/installation.html

NetCDF install issues
NetCDF Install Issues
---------------------

.. dropdown:: Troubleshooting Help
Expand Down Expand Up @@ -1945,7 +1945,7 @@ NetCDF install issues
MET_NETCDF environment variable, then run "make clean", reconfigure,
and then run "make install" and "make test" again.

Error while loading shared libraries
Error While Loading Shared Libraries
------------------------------------

.. dropdown:: Troubleshooting Help
Expand All @@ -1957,7 +1957,7 @@ Error while loading shared libraries
gsl lib (for example, */home/user/MET/gsl-2.1/lib*)
to your LD_LIBRARY_PATH.

General troubleshooting
General Troubleshooting
-----------------------

.. dropdown:: Troubleshooting Help
Expand All @@ -1971,15 +1971,15 @@ General troubleshooting
* Try rerunning with a higher verbosity level. Increasing the verbosity
level to 4 or 5 prints much more diagnostic information to the screen.

Where to get help
Where to Get Help
=================

If none of the above suggestions have helped solve your problem, help
is available through the
`METplus GitHub Discussions Forum <https://github.com/dtcenter/METplus/discussions>`_.


How to contribute code
How to Contribute Code
======================

If you have code you would like to contribute, we will gladly consider
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64 changes: 32 additions & 32 deletions docs/Users_Guide/appendixC.rst
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Expand Up @@ -52,7 +52,7 @@ Which statistics are the same, but with different names?

.. _categorical variables:

MET verification measures for categorical (dichotomous) variables
MET Verification Measures for Categorical (Dichotomous) Variables
=================================================================


Expand Down Expand Up @@ -99,7 +99,7 @@ TOTAL

The total number of forecast-observation pairs, **T**.

Base rate
Base Rate
---------

Called "O_RATE" in FHO output :numref:`table_PS_format_info_FHO`
Expand All @@ -108,7 +108,7 @@ Called "BASER" in CTS output :numref:`table_PS_format_info_CTS`

The base rate is defined as :math:`\bar{o} = \frac{n_{11} + n_{01}}{T} = \frac{n_{.1}}{T}.` This value is also known as the sample climatology, and is the relative frequency of occurrence of the event (i.e., o = 1). The base rate is equivalent to the "O" value produced by the NCEP Verification System.

Mean forecast
Mean Forecast
-------------

Called "F_RATE" in FHO output :numref:`table_PS_format_info_FHO`
Expand Down Expand Up @@ -182,7 +182,7 @@ POFD is defined as

It is the proportion of non-events that were forecast to be events. POFD is also often called the False Alarm Rate. POFD ranges from 0 to 1; a perfect forecast would have POFD = 0.

Probability of Detection of the non-event (PODn)
Probability of Detection of the Non-Event (PODn)
------------------------------------------------

Called "PODN" in CTS output :numref:`table_PS_format_info_CTS`
Expand Down Expand Up @@ -398,14 +398,14 @@ where the word expected refers to the mean value deduced from the climatology, r

SEEPS scores are expected to lie between 0 and 1, with a perfect forecast having a value of 0. Individual values can be much larger than 1. Results can be presented as a skill score by using the value of 1 – SEEPS.

MET verification measures for continuous variables
MET Verification Measures for Continuous Variables
==================================================

For continuous variables, many verification measures are based on the forecast error (i.e., **f - o**). However, it also is of interest to investigate characteristics of the forecasts, and the observations, as well as their relationship. These concepts are consistent with the general framework for verification outlined by :ref:`Murphy and Winkler, (1987) <Murphy-1987>`. The statistics produced by MET for continuous forecasts represent this philosophy of verification, which focuses on a variety of aspects of performance rather than a single measure.

The verification measures currently evaluated by the Point-Stat tool are defined and described in the subsections below. In these definitions, **f** represents the forecasts, **o** represents the observation, and **n** is the number of forecast-observation pairs.

Mean forecast
Mean Forecast
-------------

Called "FBAR" in CNT output :numref:`table_PS_format_info_CNT`
Expand All @@ -414,7 +414,7 @@ Called "FBAR" in SL1L2 output :numref:`table_PS_format_info_SL1L2`

The sample mean forecast, FBAR, is defined as :math:`\bar{f} = \frac{1}{n} \sum_{i=1}^{n} f_i`.

Mean observation
Mean Observation
----------------

Called "OBAR" in CNT output :numref:`table_PS_format_info_CNT`
Expand All @@ -423,7 +423,7 @@ Called "OBAR" in SL1L2 output :numref:`table_PS_format_info_SL1L2`

The sample mean observation is defined as :math:`\bar{o} = \frac{1}{n} \sum_{i=1}^{n} o_i`.

Forecast standard deviation
Forecast Standard Deviation
---------------------------

Called "FSTDEV" in CNT output :numref:`table_PS_format_info_CNT`
Expand All @@ -434,7 +434,7 @@ The sample variance of the forecasts is defined as

The forecast standard deviation is defined as :math:`s_f = \sqrt{s_f^2}`.

Observation standard deviation
Observation Standard Deviation
------------------------------

Called "OSTDEV" in CNT output :numref:`table_PS_format_info_CNT`
Expand All @@ -456,7 +456,7 @@ The Pearson correlation coefficient, **r**, measures the strength of linear asso

**r** can range between -1 and 1; a value of 1 indicates perfect correlation and a value of -1 indicates perfect negative correlation. A value of 0 indicates that the forecasts and observations are not correlated.

Spearman rank correlation coefficient :math:`(\rho_{s})`
Spearman Rank Correlation Coefficient :math:`(\rho_{s})`
--------------------------------------------------------

Called "SP_CORR" in CNT :numref:`table_PS_format_info_CNT`
Expand All @@ -469,7 +469,7 @@ A simpler formulation of the Spearman-rank correlation is based on differences b

Like **r**, the Spearman rank correlation coefficient ranges between -1 and 1; a value of 1 indicates perfect correlation and a value of -1 indicates perfect negative correlation. A value of 0 indicates that the forecasts and observations are not correlated.

Kendall's Tau statistic ( :math:`\tau`)
Kendall's Tau Statistic ( :math:`\tau`)
---------------------------------------

Called "KT_CORR" in CNT output :numref:`table_PS_format_info_CNT`
Expand Down Expand Up @@ -510,14 +510,14 @@ Called "MBIAS" in CNT output :numref:`table_PS_format_info_CNT`

Multiplicative bias is simply the ratio of the means of the forecasts and the observations: :math:`\text{MBIAS} = \bar{f} / \bar{o}`

Mean-squared error (MSE)
Mean-Squared Error (MSE)
------------------------

Called "MSE" in CNT output :numref:`table_PS_format_info_CNT`

MSE measures the average squared error of the forecasts. Specifically, :math:`\text{MSE} = \frac{1}{n}\sum (f_{i} - o_{i})^{2}`.

Root-mean-squared error (RMSE)
Root-Mean-Squared Error (RMSE)
------------------------------

Called "RMSE" in CNT output :numref:`table_PS_format_info_CNT`
Expand All @@ -535,7 +535,7 @@ SI is the ratio of the root mean squared error to the average observation value,

Smaller values of SI indicate better agreement between the model and observations (less scatter on scatter plot).

Standard deviation of the error
Standard Deviation of the Error
-------------------------------

Called "ESTDEV" in CNT output :numref:`table_PS_format_info_CNT`
Expand Down Expand Up @@ -590,21 +590,21 @@ The Mean Squared Error Skill Score is one minus the ratio of the forecast MSE to

.. math:: \text{MSESS} = 1 - \frac{\text{MSE}_f}{\text{MSE}_r}

Root-mean-squared Forecast Anomaly
Root-Mean-Squared Forecast Anomaly
----------------------------------

Called "RMSFA" in CNT output :numref:`table_PS_format_info_CNT`

RMSFA is the square root of the average squared forecast anomaly. Specifically, :math:`\text{RMSFA} = \sqrt{\frac{1}{n} \sum(f_{i} - c_{i})^2}`.

Root-mean-squared Observation Anomaly
Root-Mean-Squared Observation Anomaly
-------------------------------------

Called "RMSOA" in CNT output :numref:`table_PS_format_info_CNT`

RMSOA is the square root of the average squared observation anomaly. Specifically, :math:`\text{RMSOA} = \sqrt{\frac{1}{n} \sum(o_{i} - c_{i})^2}`.

Percentiles of the errors
Percentiles of the Errors
-------------------------

Called "E10", "E25", "E50", "E75", "E90" in CNT output :numref:`table_PS_format_info_CNT`
Expand Down Expand Up @@ -636,7 +636,7 @@ The uncentered anomaly correlation coefficient (ANOM_CORR_UNCNTR) which does not

Anomaly correlation can range between -1 and 1; a value of 1 indicates perfect correlation and a value of -1 indicates perfect negative correlation. A value of 0 indicates that the forecast and observed anomalies are not correlated.

Partial Sums lines (SL1L2, SAL1L2, VL1L2, VAL1L2)
Partial Sums Lines (SL1L2, SAL1L2, VL1L2, VAL1L2)
-------------------------------------------------

:numref:`table_PS_format_info_SL1L2`, :numref:`table_PS_format_info_SAL1L2`, :numref:`table_PS_format_info_VL1L2`, and :numref:`table_PS_format_info_VAL1L2`
Expand All @@ -647,7 +647,7 @@ The partial sums can be accumulated over individual cases to produce statistics

*Minimally sufficient* statistics are those that condense the data most, with no loss of information. Statistics based on L1 and L2 norms allow for good compression of information. Statistics based on other norms, such as order statistics, do not result in good compression of information. For this reason, statistics such as RMSE are often preferred to statistics such as the median absolute deviation. The partial sums are not sufficient for order statistics, such as the median or quartiles.

Scalar L1 and L2 values
Scalar L1 and L2 Values
-----------------------

Called "FBAR", "OBAR", "FOBAR", "FFBAR", and "OOBAR" in SL1L2 output :numref:`table_PS_format_info_SL1L2`
Expand All @@ -667,7 +667,7 @@ These statistics are simply the 1st and 2nd moments of the forecasts, observatio
Some of the other statistics for continuous forecasts (e.g., RMSE) can be derived from these moments.

Scalar anomaly L1 and L2 values
Scalar Anomaly L1 and L2 Values
-------------------------------

Called "FABAR", "OABAR", "FOABAR", "FFABAR", "OOABAR" in SAL1L2 output :numref:`table_PS_format_info_SAL1L2`
Expand All @@ -685,7 +685,7 @@ Computation of these statistics requires a climatological value, c. These statis
\text{OOABAR} = \text{Mean}[(o - c)^2] = \bar{(o - c)}^2 = \frac{1}{n} \sum_{i=1}^n (o_i - c)^2
Vector L1 and L2 values
Vector L1 and L2 Values
-----------------------

Called "UFBAR", "VFBAR", "UOBAR", "VOBAR", "UVFOBAR", "UVFFBAR", "UVOOBAR" in VL1L2 output :numref:`table_PS_format_info_VL1L2`
Expand All @@ -707,7 +707,7 @@ These statistics are the moments for wind vector values, where **u** is the E-W
\text{UVOOBAR} = \text{Mean}(u_o^2 + v_o^2) = \frac{1}{n} \sum_{i=1}^n (u_{oi}^2 + v_{oi}^2)
Vector anomaly L1 and L2 values
Vector Anomaly L1 and L2 Values
-------------------------------

Called "UFABAR", "VFABAR", "UOABAR", "VOABAR", "UVFOABAR", "UVFFABAR", "UVOOABAR" in VAL1L2 output :numref:`table_PS_format_info_VAL1L2`
Expand All @@ -730,7 +730,7 @@ These statistics require climatological values for the wind vector components, :
\text{UVOOABAR} = \text{Mean}[(u_o - u_c)^2 + (v_o - v_c)^2] = \frac{1}{n} \sum_{i=1}^n ((u_{oi} - u_c)^2 + (v_{oi} - v_c)^2)
Gradient values
Gradient Values
---------------

Called "TOTAL", "FGBAR", "OGBAR", "MGBAR", "EGBAR", "S1", "S1_OG", and "FGOG_RATIO" in GRAD output :numref:`table_GS_format_info_GRAD`
Expand Down Expand Up @@ -773,7 +773,7 @@ where the weights are applied at each grid location, with values assigned accord
\text{FGOG_RATIO} = \frac{\text{FGBAR}}{\text{OGBAR}}
MET verification measures for probabilistic forecasts
MET Verification Measures for Probabilistic Forecasts
=====================================================

The results of the probabilistic verification methods that are included in the Point-Stat, Grid-Stat, and Stat-Analysis tools are summarized using a variety of measures. MET treats probabilistic forecasts as categorical, divided into bins by user-defined thresholds between zero and one. For the categorical measures, if a forecast probability is specified in a formula, the midpoint value of the bin is used. These measures include the Brier Score (BS) with confidence bounds (:ref:`Bradley, 2008 <Bradley-2008>`); the joint distribution, calibration-refinement, likelihood-base rate (:ref:`Wilks, 2011 <Wilks-2011>`); and receiver operating characteristic information. Using these statistics, reliability and discrimination diagrams can be produced.
Expand Down Expand Up @@ -843,7 +843,7 @@ A component of the Brier score. For probabilistic forecasts, uncertainty is a fu

.. math:: \text{Uncertainty} = \frac{n_{.1}}{T}(1 - \frac{n_{.1}}{T})

Brier score
Brier Score
-----------

Called "BRIER" in PSTD output :numref:`table_PS_format_info_PSTD`
Expand Down Expand Up @@ -927,7 +927,7 @@ This is the probability of an event for each forecast category :math:`p_i` (row)

.. math:: \text{Base Rate}(i) = \frac{n_{i1}}{n_{i.}} = \text{probability}(o_{i1})

Reliability diagram
Reliability Diagram
-------------------

The reliability diagram is a plot of the observed frequency of events versus the forecast probability of those events, with the range of forecast probabilities divided into categories.
Expand All @@ -940,7 +940,7 @@ The ideal forecast (i.e., one with perfect reliability) has conditional observed

Example of Reliability Diagram

Receiver operating characteristic
Receiver Operating Characteristic
---------------------------------

MET produces hit rate (POD) and false alarm rate (POFD) values for each user-specified threshold. This information can be used to create a scatter plot of POFD vs. POD. When the points are connected, the plot is generally referred to as the receiver operating characteristic (ROC) curve (also called the "relative operating characteristic" curve). See the area under the ROC curve (AUC) entry for related information.
Expand All @@ -955,7 +955,7 @@ A ROC curve shows how well the forecast discriminates between two outcomes, so i

Example of ROC Curve

Area Under the ROC curve (AUC)
Area Under the ROC Curve (AUC)
------------------------------

Called "ROC_AUC" in PSTD output :numref:`table_PS_format_info_PSTD`
Expand All @@ -968,7 +968,7 @@ The area under the curve can be estimated in a variety of ways. In MET, the simp

.. _App_C-ensemble:

MET verification measures for ensemble forecasts
MET Verification Measures for Ensemble Forecasts
================================================

RPS
Expand Down Expand Up @@ -1125,7 +1125,7 @@ The ensemble spread for a single observation is the standard deviation of the en

Note that prior to met-9.0.1, the ensemble spread of a spatial masking region was computed as the average of the spread values within that region. This algorithm was corrected in met-9.0.1 to average the ensemble variance values prior to computing the square root.

MET verification measures for neighborhood methods
MET Verification Measures for Neighborhood Methods
==================================================

The results of the neighborhood verification approaches that are included in the Grid-Stat tool are summarized using a variety of measures. These measures include the Fractions Skill Score (FSS) and the Fractions Brier Score (FBS). MET also computes traditional contingency table statistics for each combination of threshold and neighborhood window size.
Expand Down Expand Up @@ -1210,7 +1210,7 @@ The overall proportion of grid points with observed events to total grid points

.. _App_C-distance_maps:

MET verification measures for distance map methods
MET Verification Measures for Distance Map Methods
==================================================

The distance map statistics include Baddeley's :math:`\Delta` Metric, a statistic which is a true mathematical metric. The definition of a mathematical metric is included below.
Expand Down Expand Up @@ -1245,7 +1245,7 @@ In terms of distance maps, Baddeley's :math:`\Delta` is the :math:`L_{p}` norm o

The range for BADDELEY and HAUSDORFF is 0 to infinity, with a score of 0 indicating a perfect forecast.

Mean-error Distance
Mean-Error Distance
-------------------

Called "MED_FO", "MED_OF", "MED_MIN", "MED_MAX", and "MED_MEAN" in the DMAP output :numref:`table_GS_format_info_DMAP`
Expand Down
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