Clarified difference between stochastic and non-stochastic climate variables in doc and readme (#36)

Author: btschwertfeger

README.md

@@ -81,13 +81,14 @@ All methods except the `adjust_3d` function requires that the input data sets on
 Except for the variance scaling, all methods can be applied on stochastic and non-stochastic
 climate variables. Variance scaling can only be applied on non-stochastic climate variables.
 
-- Stochastic climate variables are those that are subject to random fluctuations
-  and are not predictable. They have no predictable trend or pattern. Examples of
-  stochastic climate variables include precipitation, air temperature, and humidity.
-
-- Non-stochastic climate variables, on the other hand, have clear trend and pattern histories
-  and can be readily predicted. They are often referred to as climate elements and include
-  variables such as water temperature and air pressure.
+- Non-stochastic climate variables are those that can be predicted with relative certainty based
+  on factors such as location, elevation, and season. Examples of non-stochastic climate variables
+  include air temperature, air pressure, and solar radiation.
+
+- Stochastic climate variables, on the other hand, are those that exhibit a high degree of
+  variability and unpredictability, making them difficult to forecast accurately.
+  Precipitation is an example of a stochastic climate variable because it can vary greatly in timing,
+  intensity, and location due to complex atmospheric and meteorological processes.
 
 ---
 

cmethods/__init__.py

@@ -47,30 +47,31 @@ def __init__(self, method: str, available_methods: list):
 
 class CMethods:
     """
-    The CMethods class serves a collection of bias correction procedures to adjust
-    time-series of climate data.
-
-    The following bias correction techniques are available:
-        Scaling-based techniques:
-            * Linear Scaling :func:`cmethods.CMethods.linear_scaling`
-            * Variance Scaling :func:`cmethods.CMethods.variance_scaling`
-            * Delta (change) Method :func:`cmethods.CMethods.delta_method`
-
-        Distribution-based techniques:
-            * Quantile Mapping :func:`cmethods.CMethods.quantile_mapping`
-            * Detrended Quantile Mapping :func:`cmethods.CMethods.detrended_quantile_mapping`
-            * Quantile Delta Mapping :func:`cmethods.CMethods.quantile_delta_mapping`
-
-    Except for the Variance Scaling all methods can be applied on both, stochastic and non-stochastic
-    variables. The Variance Scaling can only be applied on stochastic climate variables.
-
-    Stochastic climate variables are those that are subject to random fluctuations
-    and are not predictable. They have no predictable trend or pattern. Examples of
-    stochastic climate variables include precipitation, air temperature, and humidity.
-
-    Non-stochastic climate variables, on the other hand, have clear trend and pattern histories
-    and can be readily predicted. They are often referred to as climate elements and include
-    variables such as water temperature and air pressure.
+     The CMethods class serves a collection of bias correction procedures to adjust
+     time-series of climate data.
+
+     The following bias correction techniques are available:
+         Scaling-based techniques:
+             * Linear Scaling :func:`cmethods.CMethods.linear_scaling`
+             * Variance Scaling :func:`cmethods.CMethods.variance_scaling`
+             * Delta (change) Method :func:`cmethods.CMethods.delta_method`
+
+         Distribution-based techniques:
+             * Quantile Mapping :func:`cmethods.CMethods.quantile_mapping`
+             * Detrended Quantile Mapping :func:`cmethods.CMethods.detrended_quantile_mapping`
+             * Quantile Delta Mapping :func:`cmethods.CMethods.quantile_delta_mapping`
+
+     Except for the Variance Scaling all methods can be applied on both, stochastic and non-stochastic
+     variables. The Variance Scaling can only be applied on stochastic climate variables.
+
+    - Non-stochastic climate variables are those that can be predicted with relative certainty based
+     on factors such as location, elevation, and season. Examples of non-stochastic climate variables
+     include air temperature, air pressure, and solar radiation.
+
+     - Stochastic climate variables, on the other hand, are those that exhibit a high degree of
+       variability and unpredictability, making them difficult to forecast accurately.
+       Precipitation is an example of a stochastic climate variable because it can vary greatly in timing,
+       intensity, and location due to complex atmospheric and meteorological processes.
     """
 
     SCALING_METHODS = ["linear_scaling", "variance_scaling", "delta_method"]
@@ -397,10 +398,10 @@ def linear_scaling(
 
         **Additive**:
 
-            In Linear Scaling, the long-term monthly mean (:math:`\mu_m`) of the modeled data :math:`T_{sim,h}` is subtracted
-            from the long-term monthly mean of the reference data :math:`T_{obs,h}` at time step :math:`i`.
-            This difference in month-dependent long-term mean is than added to the long-term monthly mean for time step :math:`i`,
-            in the time-series that is to be adjusted (:math:`T_{sim,p}`).
+            In Linear Scaling, the long-term monthly mean (:math:`\mu_m`) of the modeled data :math:`X_{sim,h}` is subtracted
+            from the long-term monthly mean of the reference data :math:`X_{obs,h}` at time step :math:`i`.
+            This difference in month-dependent long-term mean is than added to the value of time step :math:`i`,
+            in the time-series that is to be adjusted (:math:`X_{sim,p}`).
 
             .. math::
 
@@ -504,7 +505,7 @@ def variance_scaling(
         of the Variance Scaling approach are shown:
 
         **(1)** First, the modeled data of the control and scenario period must be bias-corrected using
-        the Linear Scaling technique. This corrects the deviation in the mean.
+        the additive linear scaling technique. This adjusts the deviation in the mean.
 
         .. math::
 

docs/src/introduction.rst

@@ -55,18 +55,19 @@ The following bias correction techniques are available:
 
 All of these methods are intended to be applied on 1-dimensional time-series climate data.
 This module also provides the function :func:`cmethods.CMethods.adjust_3d` that enables
-the application of the desired bias correction method on 3-dimensinoal data sets.
+the application of the desired bias correction method on 3-dimensional data sets.
 
 Except for the variance scaling, all methods can be applied on stochastic and non-stochastic
 climate variables. Variance scaling can only be applied on non-stochastic climate variables.
 
-- Stochastic climate variables are those that are subject to random fluctuations
-  and are not predictable. They have no predictable trend or pattern. Examples of
-  stochastic climate variables include precipitation, air temperature, and humidity.
+- Non-stochastic climate variables are those that can be predicted with relative certainty based
+  on factors such as location, elevation, and season. Examples of non-stochastic climate variables
+  include air temperature, air pressure, and solar radiation.
 
-- Non-stochastic climate variables, on the other hand, have clear trend and pattern histories
-  and can be readily predicted. They are often referred to as climate elements and include
-  variables such as water temperature and air pressure.
+- Stochastic climate variables, on the other hand, are those that exhibit a high degree of
+  variability and unpredictability, making them difficult to forecast accurately.
+  Precipitation is an example of a stochastic climate variable because it can vary greatly in timing,
+  intensity, and location due to complex atmospheric and meteorological processes.
 
 Examples can be found in the `python-cmethods`_ repository and of course
 within this documentation.