diff --git a/README.md b/README.md index 8a0e764..5eca312 100644 --- a/README.md +++ b/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. --- diff --git a/cmethods/__init__.py b/cmethods/__init__.py index d191b37..521b9e3 100644 --- a/cmethods/__init__.py +++ b/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:: diff --git a/docs/src/introduction.rst b/docs/src/introduction.rst index 5b60742..faa7bd5 100644 --- a/docs/src/introduction.rst +++ b/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.