Package 'PEcAnAssimSequential' reference manual

Title:	PEcAn Functions Used for Ecological Forecasts and Reanalysis
Description:	The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Authors:	Mike Dietze
Maintainer:	Mike Dietze <[email protected]>
License:	BSD_3_clause + file LICENSE
Version:	1.8.0.9000
Built:	2024-11-20 21:39:13 UTC
Source:	https://github.com/PecanProject/pecan

adj.ens

Description

This functions gives weights to different ensemble members based on their likelihood during the analysis step. Then it adjusts the analysis mean estimates of state variables based on the estimated weights.

Usage

adj.ens(Pf, X, mu.f, mu.a, Pa)
adj.ens(Pf, X, mu.f, mu.a, Pa)

Arguments

`Pf`	A cov matrix of forecast state variables.
`X`	Dataframe or matrix of forecast state variables for different ensembles.
`mu.f`	A vector with forecast mean estimates of state variables.
`mu.a`	A vector with analysis mean estimates of state variables.
`Pa`	The state estimate cov matrix of analysis.

Value

Returns a vector of adjusted analysis mean estimates of state variables.

Author(s)

Michael Dietze [email protected], Ann Raiho and Hamze Dokoohaki

Aggregation Function

Description

Aggregation Function

Usage

aggregate(downscale_output, polygon_data, func = "mean")
aggregate(downscale_output, polygon_data, func = "mean")

Arguments

`downscale_output`	Raster file output from downscale_function.R. Read file in this way if stored locally: `downscale_output <- readRDS("xxx.rds")`
`polygon_data`	A spatial polygon object (e.g., an 'sf' object) that defines the spatial units for aggregation. This data should be in a coordinate reference system compatible with the raster data (e.g., "EPSG:4326").
`func`	A character string specifying the aggregation function to use (e.g., 'mean', 'sum').

Details

This function will aggregate previously downscaled carbon flux amount to a spatial unit of choice

Value

It returns the 'polygon_data' with added columns for mean and sum values of the aggregated raster data for each ensemble member.

Author(s)

Harunobu Ishii

Examples

       ## Not run: 
       # Download a shapefile of U.S. (polygon data)
       url <- "https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_state_20m.zip"
       download.file(url, destfile = "polygon/us_states.zip")
       
       # Unzip the downloaded file and save locally
       unzip("polygon/us_states.zip", exdir = "polygon/us_states")
       us_states <- st_read("polygon/us_states/cb_2020_us_state_20m.shp")
       saveRDS(us_states, "polygon/us_states.rds")
       
       # Load the saved polygon data with Massachusetts as an example
       us_states <- readRDS("polygon/us_states.rds")
       state <- "MA"
       polygon_data <- st_transform(us_states[us_states$STUSPS == state, ], crs = "EPSG:4326")
     
       # Load the downscaled raster output
       downscale_output <- readRDS("path/to/downscale_output.rds")
       
       # Slot in as argument to the aggregate function
       result <- aggregate(downscale_output, polygon_data)
       print(result)
       
## End(Not run)
## Not run: 
       # Download a shapefile of U.S. (polygon data)
       url <- "https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_state_20m.zip"
       download.file(url, destfile = "polygon/us_states.zip")
       
       # Unzip the downloaded file and save locally
       unzip("polygon/us_states.zip", exdir = "polygon/us_states")
       us_states <- st_read("polygon/us_states/cb_2020_us_state_20m.shp")
       saveRDS(us_states, "polygon/us_states.rds")
       
       # Load the saved polygon data with Massachusetts as an example
       us_states <- readRDS("polygon/us_states.rds")
       state <- "MA"
       polygon_data <- st_transform(us_states[us_states$STUSPS == state, ], crs = "EPSG:4326")
     
       # Load the downscaled raster output
       downscale_output <- readRDS("path/to/downscale_output.rds")
       
       # Slot in as argument to the aggregate function
       result <- aggregate(downscale_output, polygon_data)
       print(result)
       
## End(Not run)

alltocs

Description

This function finds all the tic functions called before and estimates the time elapsed for each one saves/appends it to a csv file.

Usage

alltocs(fname = "tocs.csv")
alltocs(fname = "tocs.csv")

Arguments

fname

string path to where the output needs to be saved as a csv file.

Value

This function writes down a csv file with three columns: 1- message sepecified in the 'tic' 2- Total elapsed time and 3- the execution time

Examples

 
## Not run: 
 library(tictoc)
 tic("Analysis")
 Sys.sleep(5)
 testfunc()
 tic("Adjustment")
 Sys.sleep(4)
 alltocs("timing.csv")  

## End(Not run)
## Not run: 
 library(tictoc)
 tic("Analysis")
 Sys.sleep(5)
 testfunc()
 tic("Adjustment")
 Sys.sleep(4)
 alltocs("timing.csv")  

## End(Not run)

Additive Log Ratio transform

Description

Additive Log Ratio transform

Usage

alr(y)
alr(y)

Arguments

y

state var

analysis_sda_block

Description

This function provides the block-based MCMC sampling approach.

Usage

analysis_sda_block(
  settings,
  block.list.all,
  X,
  obs.mean,
  obs.cov,
  t,
  nt,
  MCMC.args,
  block.list.all.pre = NULL
)
analysis_sda_block(
  settings,
  block.list.all,
  X,
  obs.mean,
  obs.cov,
  t,
  nt,
  MCMC.args,
  block.list.all.pre = NULL
)

Arguments

`settings`	pecan standard multi-site settings list.
`block.list.all`	Lists of forecast and analysis outputs for each time point of each block. If t=1, we initialize those outputs of each block with NULL from the 'sda.enkf.multisite' function.
`X`	A matrix contains ensemble forecasts with the dimensions of '[ensemble number, site number * number of state variables]'. The columns are matched with the site.ids and state variable names of the inside the 'FORECAST' object in the 'sda.enkf.multisite' script.
`obs.mean`	Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation means for each state variables of each site for each time point.
`obs.cov`	Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation covariances for all state variables of each site for each time point.
`t`	time point in format of YYYY-MM-DD.
`nt`	total length of time steps, corresponding to the 'nt' variable in the 'sda.enkf.multisite' function.
`MCMC.args`	arguments for the MCMC sampling, details can be found in the roxygen strucutre for control list in the 'sda.enkf.multisite' function.
`block.list.all.pre`	pre-existed block.list.all object for passing the aqq and bqq to the current SDA run, the default is NULL. Details can be found in the roxygen structure for 'pre_enkf_params' of the 'sda.enkf.multisite' function

Details

This function will add data and constants into each block that are needed for the MCMC sampling.

Value

It returns the 'build.block.xy' object and the analysis results.

Author(s)

Dongchen Zhang

Analysis.sda

Description

This functions uses the FUN to perform the analysis. EnKF function is developed inside the PEcAnAssimSequential package which can be sent to this function to perform the Ensemble Kalman Filter. The other option is GEF function inside the same package allowing to perform Generalized Ensemble kalman Filter.

If you're using an arbitrary function you can use the ... to send any other variables to your desired analysis function.

Usage

Analysis.sda(
  settings,
  FUN,
  Forecast = list(Pf = NULL, mu.f = NULL, Q = NULL, X = NULL),
  Observed = list(R = NULL, Y = NULL),
  H,
  extraArg,
  ...
)
Analysis.sda(
  settings,
  FUN,
  Forecast = list(Pf = NULL, mu.f = NULL, Q = NULL, X = NULL),
  Observed = list(R = NULL, Y = NULL),
  H,
  extraArg,
  ...
)

Arguments

`settings`	pecan standard settings list.
`FUN`	A Function for performing the analysis step. Two available options are: 1-EnKF and 2-GEF.
`Forecast`	A list containing the forecasts variables including Q (process variance) and X (a dataframe of forecasts state variables for different ensemble)
`Observed`	A list containing the observed variables including R (cov of observed state variables) and Y (vector of estimated mean of observed state variables)
`H`	is a matrix of 1's and 0's specifying which observations go with which variables.
`extraArg`	This argument is a list containing aqq, bqq and t. The aqq and bqq are shape parameters estimated over time for the proccess covariance and t gives the time in terms of index of obs.list. See Details.
`...`	Extra argument sent to the analysis function. In case you're using the 'GEF' function, this function requires nt, obs.mean, obs.cov, which are the total number of steps, list of observed means and list of observed cov respectively.

Value

Returns whatever the FUN is returning. In case of EnKF and GEF, this function returns a list with estimated mean and cov matrix of forecast state variables as well as mean and cov estimated as a result of assimilation/analysis .

Author(s)

Michael Dietze [email protected], Ann Raiho and Hamze Dokoohaki

assess.params

Description

Assessing parameter estimations after mapping model output to tobit space

Usage

assessParams(dat, Xt, wts = NULL, mu_f_TRUE = NULL, P_f_TRUE = NULL)
assessParams(dat, Xt, wts = NULL, mu_f_TRUE = NULL, P_f_TRUE = NULL)

Arguments

`dat`	MCMC output
`Xt`	ensemble output matrix
`wts`	ensemble weights
`mu_f_TRUE`	muf before tobit2space
`P_f_TRUE`	Pf before tobit2space

Value

make plots

Author(s)

Michael Dietze and Ann Raiho [email protected]

block_matrix

Description

This function is adopted from migest package.

Usage

block_matrix(x = NULL, b = NULL, byrow = FALSE, dimnames = NULL)
block_matrix(x = NULL, b = NULL, byrow = FALSE, dimnames = NULL)

Arguments

`x`	Vector of numbers to identify each block.
`b`	Numeric value for the size of the blocks within the matrix ordered depending on byrow
`byrow`	logical value. If FALSE (the default) the blocks are filled by columns, otherwise the blocks in the matrix are filled by rows.
`dimnames`	Character string of name attribute for the basis of the block matrix. If NULL a vector of the same length of b provides the basis of row and column names.#'.

Value

Returns a matrix with block sizes determined by the b argument. Each block is filled with the same value taken from x.

Author(s)

Guy J. Abel

block.2.vector

Description

block.2.vector

Usage

block.2.vector(block.list, X, H)
block.2.vector(block.list, X, H)

Arguments

`block.list`	lists of blocks generated by the 'build.block.xy' function.
`X`	A matrix contains ensemble forecasts.
`H`	H index created by the 'construct_nimble_H' function.

Value

It returns a list of analysis results by MCMC sampling.

Author(s)

Dongchen Zhang

build_X

Description

builds X matrix for SDA

Usage

build_X(
  out.configs,
  settings,
  new.params,
  nens,
  read_restart_times,
  outdir,
  t = 1,
  var.names,
  my.read_restart,
  restart_flag = FALSE
)
build_X(
  out.configs,
  settings,
  new.params,
  nens,
  read_restart_times,
  outdir,
  t = 1,
  var.names,
  my.read_restart,
  restart_flag = FALSE
)

Arguments

`out.configs`	object created for build_X passed from sda.enkf_MultiSite
`settings`	settings object, passed from sda.enkf_MultiSite
`new.params`	object created from sda_matchparam, passed from sda.enkf_MultiSite
`nens`	number of ensemble members i.e. runs
`read_restart_times`	passed from sda.enkf_MultiSite
`outdir`	location of previous run output folder containing .nc files
`t`	Default t=1, for function to work within time loop
`var.names`	list of state variables taken from settings object
`my.read_restart`	object that points to the model restart function i.e. read_restart.SIPNET
`restart_flag`	flag if it's a restart stage. Default is FALSE.

Value

X ready to be passed to SDA Analysis code

Author(s)

Alexis Helgeson

build.block.xy

Description

This function split long vector and covariance matrix into blocks corresponding to the localization.

Usage

build.block.xy(settings, block.list.all, X, obs.mean, obs.cov, t)
build.block.xy(settings, block.list.all, X, obs.mean, obs.cov, t)

Arguments

`settings`	pecan standard multi-site settings list.
`block.list.all`	List contains nt empty sub-elements.
`X`	A matrix contains ensemble forecasts.
`obs.mean`	List of dataframe of observation means, named with observation datetime.
`obs.cov`	List of covariance matrices of state variables , named with observation datetime.
`t`	time point.

Details

This function will add data and constants into each block that are needed for the MCMC sampling.

Value

It returns the 'build.block.xy' object with data and constants filled in.

Author(s)

Dongchen Zhang

Weighted conjugate wishart

Description

Weighted conjugate wishart

Usage

conj_wt_wishart_sampler(model, mvSaved, target, control)
conj_wt_wishart_sampler(model, mvSaved, target, control)

Arguments

`model`	model
`mvSaved`	copied to
`target`	thing being targetted
`control`	unused

Construc_H

Description

This function creates a matrix mapping obsereved data to their forecast state variable.

Usage

Construct_H(choose, Y, X)
Construct_H(choose, Y, X)

Arguments

`choose`	a vector of observations indices oredered based on their appearances in the list of state variable names.
`Y`	vector of observations
`X`	Dataframe or matrix of forecast state variables for different ensembles.

Value

This returns a matrix specifying which observation go with which state variables.

Author(s)

Hamze Dokoohaki

construct_nimble_H

Description

This function is an upgrade to the Construct.H.multisite function which provides the index by different criteria.

Usage

construct_nimble_H(site.ids, var.names, obs.t, pft.path = NULL, by = "single")
construct_nimble_H(site.ids, var.names, obs.t, pft.path = NULL, by = "single")

Arguments

`site.ids`	a vector name of site ids
`var.names`	vector names of state variable names
`obs.t`	list of vector of means for the time t for different sites.
`pft.path`	physical path to the pft.csv file.
`by`	criteria, it supports by variable, site, pft, all, and single Q.

Value

Returns one vector containing index for which Q to be estimated for which variable, and the other vector gives which state variable has which observation (= element.W.Data).

Author(s)

Dongchen Zhang

Construct.H.multisite

Description

This function is makes the blocked mapping function.

Usage

Construct.H.multisite(site.ids, var.names, obs.t.mean)
Construct.H.multisite(site.ids, var.names, obs.t.mean)

Arguments

`site.ids`	a vector name of site ids
`var.names`	vector names of state variable names
`obs.t.mean`	list of vector of means for the time t for different sites.

Value

Returns a matrix with block sizes determined by the b argument. Each block is filled with the same value taken from x.

Author(s)

Hamze

Construct.R

Description

Make sure that both lists are named with siteids.

Usage

Construct.R(site.ids, var.names, obs.t.mean, obs.t.cov)
Construct.R(site.ids, var.names, obs.t.mean, obs.t.cov)

Arguments

`site.ids`	a vector name of site ids
`var.names`	vector names of state variable names
`obs.t.mean`	list of vector of means for the time t for different sites.
`obs.t.cov`	list of list of cov for the time for different sites.

Value

This function returns a list with Y and R ready to be sent to the analysis functions.

Author(s)

Hamze Dokoohaki

Contruct.Pf

Description

The argument X needs to have an attribute pointing the state variables to their corresponding site. This attribute needs to be called 'Site'. At the moment, the cov between state variables at blocks defining the cov between two sites are assumed zero.

Usage

Contruct.Pf(
  site.ids,
  var.names,
  X,
  localization.FUN = NULL,
  t = 1,
  blocked.dis = NULL,
  ...
)
Contruct.Pf(
  site.ids,
  var.names,
  X,
  localization.FUN = NULL,
  t = 1,
  blocked.dis = NULL,
  ...
)

Arguments

`site.ids`	a vector name of site ids.
`var.names`	vector names of state variable names.
`X`	a matrix of state variables. In this matrix rows represent ensembles, while columns show the variables for different sites.
`localization.FUN`	This is the function that performs the localization of the Pf matrix and it returns a localized matrix with the same dimensions.
`t`	not used
`blocked.dis`	passed to 'localization.FUN'
`...`	passed to 'localization.FUN'

Value

It returns the var-cov matrix of state variables at multiple sites.

Author(s)

Hamze Dokoohaki

Title Identify pft for each site of a multi-site settings using NLCD and Eco-region

Description

Title Identify pft for each site of a multi-site settings using NLCD and Eco-region

Usage

Create_Site_PFT_CSV(settings, Ecoregion, NLCD, con)
Create_Site_PFT_CSV(settings, Ecoregion, NLCD, con)

Arguments

`settings`	a multi-site settings
`Ecoregion`	path of Ecoregion data (*.shp)
`NLCD`	path of NLCD img file
`con`	connection to bety

Value

pft info with each site

Examples

## Not run: 
 NLCD <- file.path(
   "/fs", "data1", "pecan.data", "input",
   "nlcd_2001_landcover_2011_edition_2014_10_10",
   "nlcd_2001_landcover_2011_edition_2014_10_10.img")
 Ecoregion <- file.path(
   "/projectnb", "dietzelab", "dongchen",
   "All_NEON_SDA", "NEON42", "eco-region", "us_eco_l3_state_boundaries.shp")
 settings <- PEcAn.settings::read.settings(
   "/projectnb/dietzelab/dongchen/All_NEON_SDA/NEON42/pecan.xml")
 con <- PEcAn.DB::db.open(settings$database$bety)
   site_pft_info <- Create_Site_PFT_CSV(settings, Ecoregion, NLCD, con)

## End(Not run)

## Not run: 
 NLCD <- file.path(
   "/fs", "data1", "pecan.data", "input",
   "nlcd_2001_landcover_2011_edition_2014_10_10",
   "nlcd_2001_landcover_2011_edition_2014_10_10.img")
 Ecoregion <- file.path(
   "/projectnb", "dietzelab", "dongchen",
   "All_NEON_SDA", "NEON42", "eco-region", "us_eco_l3_state_boundaries.shp")
 settings <- PEcAn.settings::read.settings(
   "/projectnb/dietzelab/dongchen/All_NEON_SDA/NEON42/pecan.xml")
 con <- PEcAn.DB::db.open(settings$database$bety)
   site_pft_info <- Create_Site_PFT_CSV(settings, Ecoregion, NLCD, con)

## End(Not run)

weighted multivariate normal density

Description

weighted multivariate normal density

Usage

dwtmnorm(x, mean, prec, wt, log = 0)
dwtmnorm(x, mean, prec, wt, log = 0)

Arguments

`x`	random variable
`mean`	mean
`prec`	precision
`wt`	weight
`log`	log

EnKF

Description

Given the Forecast and Observed this function performs the Ensemble Kalamn Filter.

Usage

EnKF(settings, Forecast, Observed, H, extraArg = NULL, ...)
EnKF(settings, Forecast, Observed, H, extraArg = NULL, ...)

Arguments

`settings`	pecan standard settings list.
`Forecast`	A list containing the forecasts variables including Q (process variance) and X (a dataframe of forecasts state variables for different ensemble)
`Observed`	A list containing the observed variables including R (cov of observed state variables) and Y (vector of estimated mean of observed state variables)
`H`	is a matrix of 1's and 0's specifying which observations go with which variables.
`extraArg`	This argument is NOT used inside this function but it is a list containing aqq, bqq and t. The aqq and bqq are shape parameters estimated over time for the proccess covariance and t gives the time in terms of index of obs.list. See Details.
`...`	Extra argument sent to the analysis function.

Value

It returns a list with estimated mean and cov matrix of forecast state variables as well as mean and cov estimated as a result of assimilation/analysis .

Author(s)

Michael Dietze [email protected], Ann Raiho and Hamze Dokoohaki

EnKF.MultiSite

Description

Given the Forecast and Observed this function performs the Ensemble Kalamn Filter.

Usage

EnKF.MultiSite(settings, Forecast, Observed, H, extraArg = NULL, ...)
EnKF.MultiSite(settings, Forecast, Observed, H, extraArg = NULL, ...)

Arguments

`settings`	pecan standard settings list.
`Forecast`	A list containing the forecasts variables including Q (process variance) and X (a dataframe of forecasts state variables for different ensemble)
`Observed`	A list containing the observed variables including R (cov of observed state variables) and Y (vector of estimated mean of observed state variables)
`H`	is a matrix of 1's and 0's specifying which observations go with which state variables.
`extraArg`	This argument is NOT used inside this function but it is a list containing aqq, bqq and t. The aqq and bqq are shape parameters estimated over time for the proccess covariance and t gives the time in terms of index of obs.list.
`...`	Extra argument sent to the analysis function.

Details

This function is different than 'EnKF' function in terms of how it creates the Pf matrix.

Value

It returns a list with estimated mean and cov matrix of forecast state variables as well as mean and cov estimated as a result of assimilation/analysis .

Author(s)

Michael Dietze [email protected], Ann Raiho and Hamze Dokoohaki

GEF

Description

Given the Forecast and Observed this function performs the Generalized Ensemble Kalamn Filter. The generalized ensemble filter follows generally the three steps of sequential state data assimilation. But, in the generalized ensemble filter we add a latent state vector that accounts for added process variance. Furthermore, instead of solving the analysis analytically like the EnKF, we have to estimate the mean analysis vector and covariance matrix with MCMC.

Usage

GEF(
  settings,
  Forecast,
  Observed,
  H,
  extraArg,
  nitr = 50000,
  nburnin = 10000,
  ...
)

GEF.MultiSite(settings, Forecast, Observed, H, extraArg, ...)
GEF(
  settings,
  Forecast,
  Observed,
  H,
  extraArg,
  nitr = 50000,
  nburnin = 10000,
  ...
)

GEF.MultiSite(settings, Forecast, Observed, H, extraArg, ...)

Arguments

`settings`	pecan standard settings list.
`Forecast`	A list containing the forecasts variables including Q (process variance) and X (a dataframe of forecast state variables for different ensemble)
`Observed`	A list containing the observed variables including R (cov of observed state variables) and Y (vector of estimated mean of observed state variables)
`H`	not used
`extraArg`	This argument is a list containing aqq, bqq and t. The aqq and bqq are shape parameters estimated over time for the process covariance and t gives the time in terms of index of obs.list. See Details.
`nitr`	Number of iterations to run each MCMC chain.
`nburnin`	Number of initial, pre-thinning, MCMC iterations to discard.
`...`	This function requires nt, obs.mean, obs.cov, which are the total number of steps, list of observed means and list of observed cov respectively.

Value

It returns a list with estimated mean and cov matrix of forecast state variables as well as mean and cov estimated as a result of assimilation/analysis .

Author(s)

Michael Dietze [email protected], Ann Raiho and Hamze Dokoohaki

multisite TWEnF

Description

multisite TWEnF

Usage

GEF.MultiSite.Nimble
GEF.MultiSite.Nimble

Format

TBD

get_ensemble_weights

Description

Creates file of ensemble weights in format needed for SDA

Usage

get_ensemble_weights(settings, time_do)
get_ensemble_weights(settings, time_do)

Arguments

`settings`	PEcAn settings object
`time_do`	Give user specific time so you don't have to have it be annual

Value

NONE

Author(s)

Ann Raiho [email protected]

GrabFillMatrix

Description

GrabFillMatrix

Usage

GrabFillMatrix(M, ind, M1 = NULL)
GrabFillMatrix(M, ind, M1 = NULL)

Arguments

`M`	source matrix that will be either subtracted or filled in.
`ind`	vector of index that of where to be subtracted or filled in.
`M1`	additional matrix used to fill in the source matrix, the default it NULL.

Details

This function helps subtract or fill in a matrix given the index.

Author(s)

Dongchen Zhang

hop_test

Description

Hop test. This script tests that the model successfully reads it's own restart and can restart without loss of information.

Usage

hop_test(settings, ens.runid = NULL, nyear)
hop_test(settings, ens.runid = NULL, nyear)

Arguments

`settings`	SDA PEcAn settings object
`ens.runid`	run id. If not provided, is looked up from [settings$outdir]/runs.txt
`nyear`	number of years to run hop test over

Value

NONE

Author(s)

Ann Raiho [email protected]

Internal functions for plotting SDA outputs. Interactive, post analysis time-series and bias plots in base plotting system and ggplot

Description

Internal functions for plotting SDA outputs. Interactive, post analysis time-series and bias plots in base plotting system and ggplot

Usage

interactive.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.timeser.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.bias.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.bias.plotting.sda.corr(t, obs.times, X, aqq, bqq)

post.analysis.ggplot(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS,
  plot.title = NULL
)

post.analysis.ggplot.violin(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS,
  plot.title = NULL
)

post.analysis.multisite.ggplot(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  FORECAST,
  ANALYSIS,
  plot.title = NULL,
  facetg = FALSE,
  readsFF = NULL,
  Add_Map = FALSE
)

SDA_timeseries_plot(
  ANALYSIS,
  FORECAST,
  obs.mean = NULL,
  obs.cov = NULL,
  outdir,
  pft.path = NULL,
  by = "site",
  types = c("FORECAST", "ANALYSIS", "OBS"),
  CI = c(0.025, 0.975),
  unit = list(AbvGrndWood = "Mg/ha", LAI = "m2/m2", SoilMoistFrac = "", TotSoilCarb =
    "kg/m2"),
  style = list(general_color = c(FORECAST = "blue", ANALYSIS = "red", OBS = "black"),
    fill_color = c(FORECAST = "yellow", ANALYSIS = "green", OBS = "grey"), title_color =
    "red"),
  PDF_w = 20,
  PDF_h = 16,
  t.inds = NULL
)
interactive.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.timeser.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.bias.plotting.sda(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS
)

postana.bias.plotting.sda.corr(t, obs.times, X, aqq, bqq)

post.analysis.ggplot(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS,
  plot.title = NULL
)

post.analysis.ggplot.violin(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  obs,
  X,
  FORECAST,
  ANALYSIS,
  plot.title = NULL
)

post.analysis.multisite.ggplot(
  settings,
  t,
  obs.times,
  obs.mean,
  obs.cov,
  FORECAST,
  ANALYSIS,
  plot.title = NULL,
  facetg = FALSE,
  readsFF = NULL,
  Add_Map = FALSE
)

SDA_timeseries_plot(
  ANALYSIS,
  FORECAST,
  obs.mean = NULL,
  obs.cov = NULL,
  outdir,
  pft.path = NULL,
  by = "site",
  types = c("FORECAST", "ANALYSIS", "OBS"),
  CI = c(0.025, 0.975),
  unit = list(AbvGrndWood = "Mg/ha", LAI = "m2/m2", SoilMoistFrac = "", TotSoilCarb =
    "kg/m2"),
  style = list(general_color = c(FORECAST = "blue", ANALYSIS = "red", OBS = "black"),
    fill_color = c(FORECAST = "yellow", ANALYSIS = "green", OBS = "grey"), title_color =
    "red"),
  PDF_w = 20,
  PDF_h = 16,
  t.inds = NULL
)

Arguments

`settings`	pecan standard settings list.
`t`	current time - int number giving the position of the current time in obs.time.
`obs.times`	vector of dates of measurements
`obs.mean`	obs.mean
`obs.cov`	obs.cov
`obs`	list containing the mean and cov object
`X`	dataframe of state variables for each ensemble
`FORECAST`	Forecast object from the sda.output.Rdata.
`ANALYSIS`	Analysis object from the sda.output.Rdata.
`aqq`, `bqq`	shape parameters estimated over time for the process covariance
`plot.title`	character giving the title for post visualization ggplots
`facetg`	logical: Create a subpanel for each variable?
`readsFF`	optional forward forecast
`Add_Map`	Bool variable decide if we want to export the GIS map of Ecoregion.
`outdir`	physical path where the pdf will be stored.
`pft.path`	Physical path of pft.csv file to allow by = pft option.
`by`	arrange figures by var, pft, or site.
`types`	data types that shown in the figure.
`CI`	range of confidence interval.
`unit`	list of unit used for y axis label.
`style`	color option.
`PDF_w`	width of exported PDF file, passed on to 'base::pdf()'.
`PDF_h`	height of exported PDF file, passed on to 'base::pdf()'.
`t.inds`	index of period that will be plotted.

Author(s)

Dongchen Zhang

inverse of ALR transform

Description

inverse of ALR transform

Usage

inv.alr(alr)
inv.alr(alr)

Arguments

alr

state var

load_data_paleon_sda

Description

Load data function for paleon SDA data products

Usage

load_data_paleon_sda(settings)
load_data_paleon_sda(settings)

Arguments

settings

PEcAn SDA settings object

Value

obs.mean and obs.cov for sda.enkf

Author(s)

Ann Raiho [email protected]

load_nimble

Description

This functions is internally used to register a series of nimble functions inside GEF analysis function.

Usage

y_star_create(X)
y_star_create(X)

Arguments

X

state var

Author(s)

Ann Raiho, Hamze Dokoohaki

Local.support

Description

distance.mat matrix doesn't need to be just the physical distance, however it represent a measure of similarity between state variables in different sites.

Usage

Local.support(Pf, distance.mat, scalef = 1)
Local.support(Pf, distance.mat, scalef = 1)

Arguments

`Pf`	Forecast error covariance matrix
`distance.mat`	is matrix of distances between sites.
`scalef`	scalef is a numeric value that requires tuning and it controls the shape of the corrolation function

Value

It returns a localized covariance matrix by taking a Schur product between Pf and a corrolation function

Author(s)

Hamze Dokoohaki

matrix_network

Description

matrix_network

Usage

matrix_network(mat)
matrix_network(mat)

Arguments

mat

a boolean matrix representing the interactions between any sites.

Value

It returns lists of index representing each network.

Author(s)

Dongchen Zhang

MCMC_block_function

Description

MCMC_block_function

Usage

MCMC_block_function(block)
MCMC_block_function(block)

Arguments

block

each block within the 'block.list' lists.

Value

It returns the 'block' object with analysis results filled in.

Author(s)

Dongchen Zhang

MCMC_function

Description

MCMC_function

Usage

MCMC_function(data)
MCMC_function(data)

Arguments

data

list containing everything needed for the MCMC sampling.

Details

This function replace the previous code where implenmented the MCMC sampling part, which allows the MCMC sampling of multiple chains under parallel mode.

Author(s)

Michael Dietze [email protected], Ann Raiho, Hamze Dokoohaki, and Dongchen Zhang.

MCMC_Init

Description

MCMC_Init

Usage

MCMC_Init(block.list, X)
MCMC_Init(block.list, X)

Arguments

`block.list`	lists of blocks generated by the 'build.block.xy' function.
`X`	A matrix contains ensemble forecasts.

Details

This function helps create initial conditions for the MCMC sampling.

Value

It returns the 'block.list' object with initial conditions filled in.

Author(s)

Dongchen Zhang

metSplit

Description

metSplit

Usage

metSplit(
  conf.settings,
  inputs,
  settings,
  model,
  no_split = FALSE,
  obs.times,
  t,
  nens,
  restart_flag = FALSE,
  my.split_inputs
)
metSplit(
  conf.settings,
  inputs,
  settings,
  model,
  no_split = FALSE,
  obs.times,
  t,
  nens,
  restart_flag = FALSE,
  my.split_inputs
)

Arguments

`conf.settings`	listed multisite settings object generated by sda.enkf_MultiSite
`inputs`	listed object containing met ensemble members generated by input.ens.gen
`settings`	settings object passed to sda.enkf_MultiSite
`model`	model name ex. SIPNET
`no_split`	TRUE/FALSE if model requires met split
`obs.times`	matrix of dates used for assimilation
`t`	number of dates in obs.times
`nens`	number of ensemble members, taken from settings object
`restart_flag`	TRUE/FALSE taken from restart arguement
`my.split_inputs`	generated by sda.enkf_MultiSite ex. split_inputs.SIPNET

Value

input.split object with split met filepaths

Author(s)

Alexis Helgeson

convert from timestep to actual time points. supports year, month, week, and day as time unit.

Description

convert from timestep to actual time points. supports year, month, week, and day as time unit.

Usage

obs_timestep2timepoint(start.date, end.date, timestep)
obs_timestep2timepoint(start.date, end.date, timestep)

Arguments

`start.date`	start date when the first observation was taken.
`end.date`	end date when the last observation was taken.
`timestep`	a list includes time unit and number of time unit per timestep.

Value

timepoints from start to end date given the number of time unit per timestep.

Author(s)

Dongchen Zhang

Obs.data.prepare.MultiSite

Description

Obs.data.prepare.MultiSite

Usage

Obs.data.prepare.MultiSite(obs.path, site.ids)
Obs.data.prepare.MultiSite(obs.path, site.ids)

Arguments

`obs.path`	Path to the obs data which is expected to be an .Rdata.
`site.ids`	a character vector of site ids which need to be extracted.

Value

a list of observed mean and cov as the SDA expected it to be.

outlier.detector.boxplot

Description

This function performs a simple outlier replacement on all the columns of dataframes inside a list

Usage

outlier.detector.boxplot(X)
outlier.detector.boxplot(X)

Arguments

`X`	A list of dataframes

Value

A list the same dimension as X, with each column of each dataframe modified by replacing outlier points with the column median

piecew.poly.local

Description

5th order piecewise polynomial adopted from Data assimilation for spatio-temporal processes - p250 - Sebastian Reich

Usage

piecew.poly.local(Pf, distance.mat, scalef = 2)
piecew.poly.local(Pf, distance.mat, scalef = 2)

Arguments

`Pf`	Forecast error covariance matrix
`distance.mat`	is matrix of distances between sites.
`scalef`	scalef is a numeric value that requires tuning and it controls the shape of the corrolation function

Value

It returns a localized covariance matrix by taking a Schur product between Pf and a corrolation function

Author(s)

Hamze Dokoohaki

SDA observation preparation function for LAI and AGB

Description

SDA observation preparation function for LAI and AGB

Usage

Prep_OBS_SDA(settings, out_dir, AGB_dir, Search_Window = 30)
Prep_OBS_SDA(settings, out_dir, AGB_dir, Search_Window = 30)

Arguments

`settings`	multi.settings objects that contains multiple sites info
`out_dir`	output dir
`AGB_dir`	AGB data dir
`Search_Window`	search window for locate available LAI values

Value

mean and covariance of observations

Remote_Sync_launcher

Description

Remote_Sync_launcher

Usage

Remote_Sync_launcher(settingPath, remote.path, PID)
Remote_Sync_launcher(settingPath, remote.path, PID)

Arguments

`settingPath`	Path to your local setting .
`remote.path`	Path generated by SDA_remote_launcher which shows the path to your remote SDA run.
`PID`	PID generated by SDA_remote_launcher which shows the active PID running your SDA job.

rescaling_stateVars

Description

This function uses a set of scaling factors defined in the pecan XML to scale a given matrix

Usage

rescaling_stateVars(settings, X, multiply = TRUE)
rescaling_stateVars(settings, X, multiply = TRUE)

Arguments

`settings`	pecan xml settings where state variables have the scaling_factor tag
`X`	Any Matrix with column names as variable names
`multiply`	TRUE = multiplication, FALSE = division

Value

rescaled Matrix

random weighted multivariate normal

Description

random weighted multivariate normal

Usage

rwtmnorm(n, mean, prec, wt)
rwtmnorm(n, mean, prec, wt)

Arguments

`n`	sample size
`mean`	mean
`prec`	precision
`wt`	weight

Sample meteorological ensembles

Description

Sample meteorological ensembles

Usage

sample_met(settings, nens = 1)
sample_met(settings, nens = 1)

Arguments

`settings`	PEcAn settings list
`nens`	number of ensemble members to be sampled

sample parameters

Description

sample parameters

Usage

sample.parameters(ne, settings, con)
sample.parameters(ne, settings, con)

Arguments

`ne`	number of ensemble members
`settings`	PEcAn settings object
`con`	PEcAn database connection

Value

data frame of sampled parameters from the posterior distribution

Author(s)

Michael Dietze [email protected]

sampler toggling

Description

sampler toggling

Usage

sampler_toggle(model, mvSaved, target, control)
sampler_toggle(model, mvSaved, target, control)

Arguments

`model`	model
`mvSaved`	copied to
`target`	thing being targetted
`control`	unused

SDA_control

Description

SDA_control

Usage

SDA_control(
  trace = TRUE,
  ForewardForecast = FALSE,
  interactivePlot = FALSE,
  TimeseriesPlot = FALSE,
  BiasPlot = FALSE,
  plot.title = NULL,
  facet.plots = FALSE,
  debug = FALSE,
  pause = FALSE,
  Profiling = FALSE,
  OutlierDetection = FALSE
)
SDA_control(
  trace = TRUE,
  ForewardForecast = FALSE,
  interactivePlot = FALSE,
  TimeseriesPlot = FALSE,
  BiasPlot = FALSE,
  plot.title = NULL,
  facet.plots = FALSE,
  debug = FALSE,
  pause = FALSE,
  Profiling = FALSE,
  OutlierDetection = FALSE
)

Arguments

`trace`	Logical if code should print out the progress of SDA .
`ForewardForecast`	Logical if the foreward forecast estimates needs to be read and visualized in time series plots.
`interactivePlot`	Logical if time series plots need to be generated.
`TimeseriesPlot`	Logical if time series plots need to be generated.
`BiasPlot`	Logical if bias plots need to be generated
`plot.title`	Character defining the title of times series plots
`facet.plots`	Logical if the timeseries plots should be faceted based on state variables
`debug`	Logical if the code should stop at some milestones and open an interactive debugging environment
`pause`	Logical if code needs to be paused and wait for further instruction after the analysis step
`Profiling`	Logical if code should keep track of how much time each step took
`OutlierDetection`	Logical if TRUE then a simple method will be used to replace simulations of outside 3IQR with the median of ensembles.

Value

list of all arguments needed to setup the SDA function

SDA Downscale Function

Description

This function uses either Random Forest or Convolutional Neural Network model based on the model_type parameter.

Usage

SDA_downscale(
  preprocessed,
  date,
  carbon_pool,
  covariates,
  model_type = "rf",
  seed = NULL
)
SDA_downscale(
  preprocessed,
  date,
  carbon_pool,
  covariates,
  model_type = "rf",
  seed = NULL
)

Arguments

`preprocessed`	List. Preprocessed data returned as an output from the SDA_downscale_preprocess function.
`date`	Date. If SDA site run, format is yyyy/mm/dd; if NEON, yyyy-mm-dd. Restricted to years within file supplied to 'preprocessed' from the 'data_path'.
`carbon_pool`	Character. Carbon pool of interest. Name must match carbon pool name found within file supplied to 'preprocessed' from the 'data_path'.
`covariates`	SpatRaster stack. Used as predictors in downscaling. Layers within stack should be named. Recommended that this stack be generated using 'covariates' instructions in assim.sequential/inst folder
`model_type`	Character. Either "rf" for Random Forest or "cnn" for Convolutional Neural Network. Default is Random Forest.
`seed`	Numeric or NULL. Optional seed for random number generation. Default is NULL.

Details

This function will downscale forecast data to unmodeled locations using covariates and site locations

Value

A list containing the training and testing data sets, models, predicted maps for each ensemble member, and predictions for testing data.

Author(s)

Joshua Ploshay, Sambhav Dixit

SDA　Downscale Function for Hourly Data

Description

This function uses the randomForest model to downscale forecast data (hourly) to unmodeled locations using covariates and site locations

Usage

SDA_downscale_hrly(nc_file, coords, yyyy, covariates)
SDA_downscale_hrly(nc_file, coords, yyyy, covariates)

Arguments

`nc_file`	In quotes, file path for .nc containing ensemble data.
`coords`	In quotes, file path for .csv file containing the site coordinates, columns named "lon" and "lat".
`yyyy`	In string, format is yyyy(year of interest)
`covariates`	SpatRaster stack, used as predictors in randomForest. Layers within stack should be named. Recommended that this stack be generated using 'covariates' instructions in assim.sequential/inst folder

Value

It returns the 'downscale_output' list containing lists for the training and testing data sets, models, and predicted maps for each ensemble member.

Author(s)

Harunobu Ishii

Calculate Metrics for Downscaling Results

Description

This function takes the output from the SDA_downscale function and computes various performance metrics for each ensemble. It provides a way to evaluate the accuracy of the downscaling results without modifying the main downscaling function.

Usage

SDA_downscale_metrics(downscale_output, carbon_pool)
SDA_downscale_metrics(downscale_output, carbon_pool)

Arguments

`downscale_output`	List. Output from the SDA_downscale function, containing data, models, maps, and predictions for each ensemble.
`carbon_pool`	Character. Name of the carbon pool used in the downscaling process.

Details

This function calculates performance metrics for the downscaling results. It computes Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared for each ensemble. The function uses the actual values from the testing data and the predictions generated during the downscaling process.

Value

A list of metrics for each ensemble, where each element contains MAE , MSE ,R_squared ,actual values from testing data and predicted values for the testing data

Author(s)

Sambhav Dixit

Preprocess Data for Downscaling

Description

This function reads and checks the input data, ensuring that the required date and carbon pool exist, and that the site coordinates are valid.

Usage

SDA_downscale_preprocess(data_path, coords_path, date, carbon_pool)
SDA_downscale_preprocess(data_path, coords_path, date, carbon_pool)

Arguments

`data_path`	Character. File path for .rds containing ensemble data.
`coords_path`	Character. File path for .csv file containing the site coordinates, with columns named "lon" and "lat".
`date`	Date. If SDA site run, format is yyyy/mm/dd; if NEON, yyyy-mm-dd. Restricted to years within the file supplied to 'data_path'.
`carbon_pool`	Character. Carbon pool of interest. Name must match the carbon pool name found within the file supplied to 'data_path'.

Details

This function ensures that the specified date and carbon pool are present in the input data. It also checks the validity of the site coordinates and aligns the number of rows between site coordinates and carbon data.

Value

A list containing The read .rds data , The cleaned site coordinates, and the preprocessed carbon data.

Author(s)

Sambhav Dixit

sda_matchparam

Description

sda_matchparam

Usage

sda_matchparam(settings, ensemble.samples, site.ids, nens)
sda_matchparam(settings, ensemble.samples, site.ids, nens)

Arguments

`settings`	settings object passed from sda.enkf_MultiSite
`ensemble.samples`	taken from sample.Rdata object
`site.ids`	character object passed from sda.enkf_MultiSite
`nens`	number of ensemble members in model runs, taken from restart$runids

Value

new.params object used to

Author(s)

Alexis Helgeson

Assembler for preparing obs.mean and obs.cov for the SDA workflow

Description

Assembler for preparing obs.mean and obs.cov for the SDA workflow

Usage

SDA_OBS_Assembler(settings)
SDA_OBS_Assembler(settings)

Arguments

settings

the settings object followed by PEcAn.settings format.

Value

list of obs.mean and obs.cov

Author(s)

Dongchen Zhang

Examples

## Not run: 
settings_dir <- "/projectnb/dietzelab/dongchen/All_NEON_SDA/NEON42/IC/pecan.xml"
settings <- PEcAn.settings::read.settings(settings_dir)
OBS <- SDA_OBS_Assembler(settings)

## End(Not run)

## Not run: 
settings_dir <- "/projectnb/dietzelab/dongchen/All_NEON_SDA/NEON42/IC/pecan.xml"
settings <- PEcAn.settings::read.settings(settings_dir)
OBS <- SDA_OBS_Assembler(settings)

## End(Not run)

SDA_remote_launcher

Description

SDA_remote_launcher

Usage

SDA_remote_launcher(settingPath, ObsPath, run.bash.args)
SDA_remote_launcher(settingPath, ObsPath, run.bash.args)

Arguments

`settingPath`	The Path to the setting that will run SDA
`ObsPath`	Path to the obs data which is expected to be an .Rdata.
`run.bash.args`	Shell commands to be run on the remote host before launching the SDA. See examples

Value

This function returns a list of two pieces of information. One the remote path that SDA is running and the PID of the active run.

Examples

## Not run: 
 # This example can be found under inst folder in the package
 library(PEcAn.all)
 library(purrr)
 
 run.bash.args <- c(
  "#$ -l h_rt=48:00:00",
  "#$ -pe omp 28 # Request a parallel environment with 4 cores",
  "#$ -l mem_per_core=1G # and 4G memory for each",
  "#$ -l buyin",
  "module load R/3.5.2",
  "module load python/2.7.13"
  )
 settingPath <-"pecan.SDA.4sites.xml"

 ObsPath <- "Obs/LandTrendr_AGB_output50s.RData"
   
 SDA_remote_launcher(settingPath, ObsPath, run.bash.args)

## End(Not run)

## Not run: 
 # This example can be found under inst folder in the package
 library(PEcAn.all)
 library(purrr)
 
 run.bash.args <- c(
  "#$ -l h_rt=48:00:00",
  "#$ -pe omp 28 # Request a parallel environment with 4 cores",
  "#$ -l mem_per_core=1G # and 4G memory for each",
  "#$ -l buyin",
  "module load R/3.5.2",
  "module load python/2.7.13"
  )
 settingPath <-"pecan.SDA.4sites.xml"

 ObsPath <- "Obs/LandTrendr_AGB_output50s.RData"
   
 SDA_remote_launcher(settingPath, ObsPath, run.bash.args)

## End(Not run)

Calculate ensemble weights for each site at time t.

Description

Calculate ensemble weights for each site at time t.

Usage

sda_weights_site(FORECAST, ANALYSIS, t, ens)
sda_weights_site(FORECAST, ANALYSIS, t, ens)

Arguments

`FORECAST`	FORECAST object built within the sda.enkf_MultiSite function.
`ANALYSIS`	ANALYSIS object built within the Analysis_sda_multisite function.
`t`	exact number of t inside the sda.enkf_MultiSite function.
`ens`	number of ensemble members.

Value

list of weights associated with each ensemble member of each site.

Author(s)

Dongchen Zhang and Hamze Dokoohaki

State Variable Data Assimilation: Ensemble Kalman Filter and Generalized ensemble filter

Description

Restart mode: Basic idea is that during a restart (primary case envisioned as an iterative forecast), a new workflow folder is created and the previous forecast for the start_time is copied over. During restart the initial run before the loop is skipped, with the info being populated from the previous run. The function then dives right into the first Analysis, then continues on like normal.

Usage

sda.enkf(
  settings,
  obs.mean,
  obs.cov,
  Q = NULL,
  restart = NULL,
  control = list(trace = TRUE, interactivePlot = TRUE, TimeseriesPlot = TRUE, BiasPlot =
    FALSE, plot.title = NULL, debug = FALSE, pause = FALSE),
  ...
)
sda.enkf(
  settings,
  obs.mean,
  obs.cov,
  Q = NULL,
  restart = NULL,
  control = list(trace = TRUE, interactivePlot = TRUE, TimeseriesPlot = TRUE, BiasPlot =
    FALSE, plot.title = NULL, debug = FALSE, pause = FALSE),
  ...
)

Arguments

`settings`	PEcAn settings object
`obs.mean`	List of dataframe of observation means, named with observation datetime.
`obs.cov`	List of covariance matrices of state variables , named with observation datetime.
`Q`	Process covariance matrix given if there is no data to estimate it.
`restart`	Used for iterative updating previous forecasts. When the restart is TRUE it read the object in SDA folder written from previous SDA.
`control`	List of flags controlling the behaviour of the SDA. trace for reporting back the SDA outcomes, interactivePlot for plotting the outcomes after each step, TimeseriesPlot for post analysis examination, BiasPlot for plotting the correlation between state variables, plot.title is the title of post analysis plots and debug mode allows for pausing the code and examining the variables inside the function.
`...`	Additional arguments, currently ignored

Value

NONE

Author(s)

Michael Dietze and Ann Raiho [email protected]

State Variable Data Assimilation: Ensemble Kalman Filter and Generalized ensemble filter

Description

Check out SDA_control function for more details on the control arguments.

Usage

sda.enkf.multisite(
  settings,
  obs.mean,
  obs.cov,
  Q = NULL,
  restart = NULL,
  pre_enkf_params = NULL,
  ensemble.samples = NULL,
  control = list(trace = TRUE, TimeseriesPlot = FALSE, debug = FALSE, pause = FALSE,
    Profiling = FALSE, OutlierDetection = FALSE, parallel_qsub = TRUE, send_email = NULL,
    keepNC = TRUE, forceRun = TRUE, run_parallel = TRUE, MCMC.args = NULL),
  ...
)
sda.enkf.multisite(
  settings,
  obs.mean,
  obs.cov,
  Q = NULL,
  restart = NULL,
  pre_enkf_params = NULL,
  ensemble.samples = NULL,
  control = list(trace = TRUE, TimeseriesPlot = FALSE, debug = FALSE, pause = FALSE,
    Profiling = FALSE, OutlierDetection = FALSE, parallel_qsub = TRUE, send_email = NULL,
    keepNC = TRUE, forceRun = TRUE, run_parallel = TRUE, MCMC.args = NULL),
  ...
)

Arguments

`settings`	PEcAn settings object
`obs.mean`	Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation means for each state variables of each site for each time point.
`obs.cov`	Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation covariances for all state variables of each site for each time point.
`Q`	Process covariance matrix given if there is no data to estimate it.
`restart`	Used for iterative updating previous forecasts. Default NULL. List object includes file path to previous runs and start date for SDA.
`pre_enkf_params`	Used for passing pre-existing time-series of process error into the current SDA runs to ignore the impact by the differences between process errors.
`ensemble.samples`	Pass ensemble.samples from outside to avoid GitHub check issues.
`control`	List of flags controlling the behavior of the SDA. 'trace' for reporting back the SDA outcomes; 'TimeseriesPlot' for post analysis examination; 'debug' decide if we want to pause the code and examining the variables inside the function; 'pause' decide if we want to pause the SDA workflow at current time point t; 'Profiling' decide if we want to export the temporal SDA outputs in CSV file; 'OutlierDetection' decide if we want to execute the outlier detection each time after the model forecasting; 'parallel_qsub' decide if we want to execute the 'qsub' job submission under parallel mode; 'send_email' contains lists for sending email to report the SDA progress; 'keepNC' decide if we want to keep the NetCDF files inside the out directory; 'forceRun' decide if we want to proceed the Bayesian MCMC sampling without observations; 'run_parallel' decide if we want to run the SDA under parallel mode for the 'future_map' function; 'MCMC.args' include lists for controling the MCMC sampling process (iteration, nchains, burnin, and nthin.).
`...`	Additional arguments, currently ignored

Details

Value

NONE

Author(s)

Michael Dietze, Ann Raiho and Alexis Helgeson [email protected]

State Variable Data Assimilation: Ensemble Kalman Filter

Description

Usage

sda.enkf.original(
  settings,
  obs.mean,
  obs.cov,
  IC = NULL,
  Q = NULL,
  adjustment = TRUE,
  restart = NULL
)
sda.enkf.original(
  settings,
  obs.mean,
  obs.cov,
  IC = NULL,
  Q = NULL,
  adjustment = TRUE,
  restart = NULL
)

Arguments

`settings`	PEcAn settings object
`obs.mean`	list of observations of the means of state variable (time X nstate)
`obs.cov`	list of observations of covariance matrices of state variables (time X nstate X nstate)
`IC`	initial conditions
`Q`	process covariance matrix given if there is no data to estimate it
`adjustment`	flag for using ensemble adjustment filter or not
`restart`	Used for iterative updating previous forecasts. This is a list that includes ens.inputs, the list of inputs by ensemble member, params, the parameters, and old_outdir, the output directory from the previous workflow. These three things are needed to ensure that if a new workflow is started that ensemble members keep there run-specific met and params. See Details

Value

NONE

Author(s)

Michael Dietze and Ann Raiho [email protected]

simple.local

Description

Adopted from Data assimilation for spatio-temporal processes - p250 - Sebastian Reich

Usage

simple.local(Pf, distance.mat, scalef = 2)
simple.local(Pf, distance.mat, scalef = 2)

Arguments

`Pf`	Forecast error covariance matrix
`distance.mat`	is matrix of distances between sites.
`scalef`	scalef is a numeric value that requires tuning and it controls the shape of the corrolation function

Value

It returns a localized covariance matrix by taking a Schur product between Pf and a corrolation function

Author(s)

Hamze Dokoohaki

tobit_model_censored

Description

tobit_model_censored

Usage

tobit_model_censored(settings, X, var.names, mu.f, Pf, t)
tobit_model_censored(settings, X, var.names, mu.f, Pf, t)

Arguments

`settings`	(list) pecan standard settings list.
`X`	(numeric) A matrix contains ensemble forecasts (ensembles * variables).
`var.names`	(character) variable names.
`mu.f`	(numeric) forecast mean values.
`Pf`	(numeric) forecast covariance matrix.
`t`	(numeric) timestep. If t=1, initial values are imputed for zero values in mu.f

Value

list with updated mu.f, pf, X, and indication of which y values are censored

TWEnF

Description

TWEnF

Usage

tobit.model
tobit.model

Format

TBD

Fit tobit prior to ensemble members

Description

Fit tobit prior to ensemble members

Usage

tobit2space.model
tobit2space.model

Format

TBD

update_q

Description

update_q

Usage

update_q(
  block.list.all,
  t,
  nt,
  aqq.Init = NULL,
  bqq.Init = NULL,
  MCMC_dat = NULL,
  block.list.all.pre = NULL
)
update_q(
  block.list.all,
  t,
  nt,
  aqq.Init = NULL,
  bqq.Init = NULL,
  MCMC_dat = NULL,
  block.list.all.pre = NULL
)

Arguments

`block.list.all`	each block within the 'block.list' lists.
`t`	time point.
`nt`	total length of time steps.
`aqq.Init`	the initial values of aqq, the default is NULL.
`bqq.Init`	the initial values of bqq, the default is NULL.
`MCMC_dat`	data frame of MCMC samples, the default it NULL.
`block.list.all.pre`	pre-existed block.list.all object for passing the aqq and bqq to the current SDA run, the default is NULL.

Value

It returns the 'block.list.all' object with initialized/updated Q filled in.

Author(s)

Dongchen Zhang

Package 'PEcAnAssimSequential'

Help Index

adj.ens

Description

Usage

Arguments

Value

Author(s)

Aggregation Function

Description

Usage

Arguments

Details

Value

Author(s)

Examples

alltocs

Description

Usage

Arguments

Value

Examples

Additive Log Ratio transform

Description

Usage

Arguments

analysis_sda_block

Description

Usage

Arguments

Details

Value

Author(s)

Analysis.sda

Description

Usage

Arguments

Value

Author(s)

assess.params

Description

Usage

Arguments

Value

Author(s)

block_matrix

Description

Usage

Arguments

Value

Author(s)

block.2.vector

Description

Usage

Arguments

Value

Author(s)

build_X

Description

Usage

Arguments

Value

Author(s)

build.block.xy

Description

Usage

Arguments

Details

Value

Author(s)

Weighted conjugate wishart

Description

Usage

Arguments

Construc_H

Description

Usage

Arguments

Value

Author(s)