From 0ad197ca8ea51776a05f20a0b1088feb9fc0e2aa Mon Sep 17 00:00:00 2001 From: "LAPTOP-FO87M896\\freya" Date: Fri, 1 Mar 2024 15:12:52 -0500 Subject: [PATCH] update md --- ...N_forecast_challenge_workshop_aquatics.Rmd | 6 +- ...ON_forecast_challenge_workshop_aquatics.md | 476 +++++++++--------- .../unnamed-chunk-13-1.png | Bin 12591 -> 14728 bytes .../unnamed-chunk-13-2.png | Bin 15233 -> 20280 bytes .../unnamed-chunk-17-1.png | Bin 39672 -> 48723 bytes .../unnamed-chunk-21-1.png | Bin 71732 -> 44601 bytes .../unnamed-chunk-29-1.png | Bin 83946 -> 209424 bytes .../unnamed-chunk-30-1.png | Bin 144074 -> 166164 bytes .../unnamed-chunk-41-1.png | Bin 9661 -> 12410 bytes .../unnamed-chunk-7-1.png | Bin 27803 -> 30558 bytes .../unnamed-chunk-7-2.png | Bin 24067 -> 25824 bytes .../unnamed-chunk-7-3.png | Bin 19649 -> 20297 bytes 12 files changed, 240 insertions(+), 242 deletions(-) diff --git a/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.Rmd b/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.Rmd index c106c071..63b986be 100644 --- a/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.Rmd +++ b/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.Rmd @@ -452,9 +452,9 @@ Questions about Challenge registration and synthesis participation can be direct This forecasting method uses a method from the `fable` R package which is installed via `fpp3` package. The `fable` package implements a range of different forecasting methods including Persistence Models, Moving Average Models, ARIMA Models and Time Series Models. The package integrates with `tidyverse` syntax and has good documentation and examples found [here](https://otexts.com/fpp3/). `fable` and `fabletools`, are installed as part of the `fpp3` package and produce and deal with `mable` (model table) and `fable` (forecast table) objects. We will also use the `tidyverse` to manipulate and visualise the target data and forecasts. -```{r, 'load packages', eval=TRUE, echo = TRUE, error=FALSE, warning=FALSE, message=FALSE} -install.packages('fpp3') # package for applying simple forecasting methods -install.packages('tsibble') # package for dealing with time series data sets and tsibble objects +```{r, 'load packages', echo = TRUE, error=FALSE, warning=FALSE, message=FALSE} +# install.packages('fpp3') # package for applying simple forecasting methods +# install.packages('tsibble') # package for dealing with time series data sets and tsibble objects library(fpp3) # package for forecasting library(tsibble) # package for dealing with time series data sets diff --git a/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.md b/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.md index bdc8b8e7..1af4f68f 100644 --- a/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.md +++ b/Submit_forecast/NEON_forecast_challenge_workshop_aquatics.md @@ -1,88 +1,64 @@ -- 1 - This R markdown document -- 2 Introduction to NEON - forecast challenge - - 2.1 Aquatics - challenge - - 2.2 - Submission requirements -- 3 - The forecasting workflow - - 3.1 Read in the - data - - 3.2 Visualise - the data -- 4 - Introducing co-variates - - 4.1 - Download co-variates - - 4.1.1 - Download historic data - - 4.1.2 Download future weather - forecasts -- 5 Model 1: Linear model - with covariates - - 5.1 - Specify forecast model - - 5.2 Convert to EFI - standard for submission - - 5.3 Submit - forecast - - 5.4 TASKS - - 5.5 Register your participation -- 6 Alternative forecasting - approaches - - 6.1 Model 2: - Persistence - - 6.1.1 Convert to EFI - standard for submission - - 6.1.2 Write the forecast to file - - 6.1.3 Submit - forecast - - 6.1.4 TASKS - - 6.2 Model 3: Climatology model - - 6.2.1 Write the - forecast for NEON EFI challenge - - 6.2.2 Submit - forecast - - 6.2.3 TASKS -- 7 Other things that might be - useful - - 7.1 How the forecasts are - scored? - - 7.2 - Other useful R packages - - 7.3 - Other weather variables - - 7.4 File format - - 7.5 Automating your - forecasting workflow - - 7.6 - Alternative methods to loop through each variable-site_id - combination +- [1 This R markdown document](#this-r-markdown-document) +- [2 Introduction to NEON forecast + challenge](#introduction-to-neon-forecast-challenge) + - [2.1 Aquatics challenge](#aquatics-challenge) + - [2.2 Submission requirements](#submission-requirements) +- [3 The forecasting workflow](#the-forecasting-workflow) + - [3.1 Read in the data](#read-in-the-data) + - [3.2 Visualise the data](#visualise-the-data) +- [4 Introducing co-variates](#introducing-co-variates) + - [4.1 Download co-variates](#download-co-variates) + - [4.1.1 Download historic data](#download-historic-data) + - [4.1.2 Download future weather + forecasts](#download-future-weather-forecasts) +- [5 Model 1: Linear model with + covariates](#model-1-linear-model-with-covariates) + - [5.1 Specify forecast model](#specify-forecast-model) + - [5.2 Convert to EFI standard for + submission](#convert-to-efi-standard-for-submission) + - [5.3 Submit forecast](#submit-forecast) + - [5.4 TASKS](#tasks) + - [5.5 Register your participation](#register-your-participation) +- [6 Alternative forecasting + approaches](#alternative-forecasting-approaches) + - [6.1 Model 2: Persistence](#model-2-persistence) + - [6.1.1 Convert to EFI standard for + submission](#convert-to-efi-standard-for-submission-1) + - [6.1.2 Write the forecast to + file](#write-the-forecast-to-file) + - [6.1.3 Submit forecast](#submit-forecast-1) + - [6.1.4 TASKS](#tasks-1) + - [6.2 Model 3: Climatology model](#model-3-climatology-model) + - [6.2.1 Write the forecast for NEON EFI + challenge](#write-the-forecast-for-neon-efi-challenge) + - [6.2.2 Submit forecast](#submit-forecast-2) + - [6.2.3 TASKS](#tasks-2) +- [7 Other things that might be + useful](#other-things-that-might-be-useful) + - [7.1 How the forecasts are + scored?](#how-the-forecasts-are-scored) + - [7.2 Other useful R packages](#other-useful-r-packages) + - [7.3 Other weather variables](#other-weather-variables) + - [7.4 File format](#file-format) + - [7.5 Automating your forecasting + workflow](#automating-your-forecasting-workflow) + - [7.6 Alternative methods to loop through each variable-site_id + combination](#alternative-methods-to-loop-through-each-variable-site_id-combination) # 1 This R markdown document -This document present workshop materials initially presented in the -Forecast Challenge part of the GLEON2022 workshop “Introduction to -real-time lake forecasting: learn, teach, and generate forecasts with -Macrosystems EDDIE modules and the NEON Forecasting Challenge.” The -materials have been modified slightly for use in additional workshops. +This document presents workshop materials to get you started on +generating forecasts specifically for submission to the EFI-NEON +Forecasting Challenge. The Challenge goal is to create a community of +practice that builds capacity for ecological forecasting by leveraging +NEON data products. The Challenge revolves around the five theme areas +that span aquatic and terrestrial systems, and population, community, +and ecosystem processes across a broad range of ecoregions that uses +data collected by NEON. Learn more about the Challenge +[here](https://projects.ecoforecast.org/neon4cast-docs/)! + +The development of these materials has been supported by NSF grants +DEB-1926388 and DBI-1933016. To complete the workshop via this markdown document the following packages will need to be installed: @@ -124,30 +100,32 @@ library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.2.3 - ## Warning: package 'tibble' was built under R version 4.2.1 + ## Warning: package 'tibble' was built under R version 4.2.3 ## Warning: package 'tidyr' was built under R version 4.2.2 - ## Warning: package 'readr' was built under R version 4.2.1 + ## Warning: package 'readr' was built under R version 4.2.3 - ## Warning: package 'purrr' was built under R version 4.2.2 + ## Warning: package 'purrr' was built under R version 4.2.3 - ## Warning: package 'dplyr' was built under R version 4.2.1 + ## Warning: package 'dplyr' was built under R version 4.2.3 - ## Warning: package 'stringr' was built under R version 4.2.2 + ## Warning: package 'stringr' was built under R version 4.2.3 ## Warning: package 'forcats' was built under R version 4.2.3 + ## Warning: package 'lubridate' was built under R version 4.2.3 + ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── - ## ✔ dplyr 1.0.10 ✔ readr 2.1.3 - ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 - ## ✔ ggplot2 3.4.2 ✔ tibble 3.1.8 - ## ✔ lubridate 1.8.0 ✔ tidyr 1.3.0 - ## ✔ purrr 1.0.1 + ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 + ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 + ## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 + ## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0 + ## ✔ purrr 1.0.2 ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag() - ## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors + ## ℹ Use the conflicted package () to force all conflicts to become errors ``` r library(lubridate) @@ -247,19 +225,19 @@ aquatic_sites <- read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast- Let’s take a look at the targets data! ## # A tibble: 11 × 4 - ## datetime site_id variable observation - ## - ## 1 2017-10-26 ARIK oxygen 5.52 - ## 2 2017-10-27 ARIK chla NA - ## 3 2017-10-27 ARIK oxygen 7.90 - ## 4 2017-10-28 ARIK chla NA - ## 5 2017-10-28 ARIK oxygen 6.01 - ## 6 2017-10-29 ARIK chla NA - ## 7 2017-10-29 ARIK oxygen 7.56 - ## 8 2017-10-30 ARIK chla NA - ## 9 2017-10-30 ARIK oxygen 8.08 - ## 10 2017-10-31 ARIK chla NA - ## 11 2017-10-31 ARIK oxygen 8.68 + ## datetime site_id variable observation + ## + ## 1 2017-04-23 ARIK temperature 12.7 + ## 2 2017-04-24 ARIK chla NA + ## 3 2017-04-24 ARIK oxygen 7.10 + ## 4 2017-04-24 ARIK temperature 14.8 + ## 5 2017-04-25 ARIK chla NA + ## 6 2017-04-25 ARIK oxygen 6.58 + ## 7 2017-04-25 ARIK temperature 15.5 + ## 8 2017-04-26 ARIK chla NA + ## 9 2017-04-26 ARIK oxygen 7.32 + ## 10 2017-04-26 ARIK temperature 13.0 + ## 11 2017-04-27 ARIK chla NA The columns of the targets file show the time step (daily for aquatics challenge), the 4 character site code (`site_id`), the variable being @@ -326,18 +304,18 @@ sites. The 3 types of data are as follows: - stage_2: a processed version of Stage 1 in which fluxes are standardized to per second rates, fluxes and states are interpolated to 1 hour intervals and variables are renamed to match conventions. - We recommend this for obtaining future weather by using - `neon4cast::noaa_stage2()`. Future weather forecasts include a - 30-member ensemble of equally likely future weather conditions. + We recommend this for obtaining future weather. Future weather + forecasts include a 30-member ensemble of equally likely future + weather conditions. - stage_3: can be viewed as the “historical” weather and is combination of day 1 weather forecasts (i.e., when the forecasts are - most accurate). You can download this “stacked” NOAA product using - `neon4cast::noaa_stage3()`. + most accurate). -These functions create a connection to the dataset hosted on the -eco4cast server. To download the data you have to tell the function to -`collect()` it. These data set can be subsetted and filtered using -`dplyr` functions prior to download to limit the memory usage. +This code create a connection to the dataset hosted on the eco4cast +server (`neon4cast-drivers/noaa/gefs-v12`) using `arrow` functions. To +download the data you have to tell the function to `collect()` it. These +data set can be subsetted and filtered using `dplyr` functions prior to +download to limit the memory usage. You can read more about the NOAA forecasts available for the NEON sites [here:](https://projects.ecoforecast.org/neon4cast-docs/Shared-Forecast-Drivers.html) @@ -352,12 +330,12 @@ it accesses the NOAA data. ``` r # past stacked weather -df_past <- neon4cast::noaa_stage3() +noaa_past_s3 <- neon4cast::noaa_stage3() variables <- c("air_temperature") #Other variable names can be found at https://projects.ecoforecast.org/neon4cast-docs/Shared-Forecast-Drivers.html#stage-3 -noaa_past <- df_past |> +noaa_past <- noaa_past_s3 |> dplyr::filter(site_id %in% lake_sites$field_site_id, datetime >= ymd('2017-01-01'), variable %in% variables) |> @@ -366,21 +344,21 @@ noaa_past <- df_past |> noaa_past ``` - ## # A tibble: 9,357,908 × 9 - ## datetime site_id longitude latitude family parameter variable - ## - ## 1 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 1 air_temper… - ## 2 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 2 air_temper… - ## 3 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 3 air_temper… - ## 4 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 4 air_temper… - ## 5 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 5 air_temper… - ## 6 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 6 air_temper… - ## 7 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 7 air_temper… - ## 8 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 8 air_temper… - ## 9 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 9 air_temper… - ## 10 2020-09-25 00:00:00 PRLA -99.1 47.2 ensemble 10 air_temper… - ## # ℹ 9,357,898 more rows - ## # ℹ 2 more variables: height , prediction + ## # A tibble: 6,531,049 × 7 + ## parameter datetime variable prediction family reference_datetime + ## + ## 1 0 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 2 1 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 3 2 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 4 3 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 5 4 2020-09-24 00:00:00 air_tempe… 298. ensem… NA + ## 6 5 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 7 6 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 8 7 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 9 8 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## 10 9 2020-09-24 00:00:00 air_tempe… 297. ensem… NA + ## # ℹ 6,531,039 more rows + ## # ℹ 1 more variable: site_id This is a stacked ensemble forecast of the one day ahead forecasts. To get an estimate of the historic conditions we can take a mean of these @@ -413,16 +391,14 @@ forecast by forecasting multiple (31) future water temperatures. forecast_date <- Sys.Date() noaa_date <- forecast_date - days(1) -df_future <- neon4cast::noaa_stage2() - +noaa_future_s3 <- neon4cast::noaa_stage2(start_date = as.character(noaa_date)) variables <- c("air_temperature") -noaa_future <- df_future |> - dplyr::filter(reference_datetime == noaa_date, - datetime >= forecast_date, +noaa_future <- noaa_future_s3 |> + dplyr::filter(datetime >= forecast_date, site_id %in% lake_sites$field_site_id, variable %in% variables) |> - dplyr::collect() + collect() ``` The forecasts are hourly and we are interested in using daily mean air @@ -447,21 +423,21 @@ noaa_future_daily <- noaa_future |> noaa_future_daily ``` - ## # A tibble: 7,462 × 4 - ## # Groups: datetime, site_id, parameter [7,462] + ## # A tibble: 7,595 × 4 + ## # Groups: datetime, site_id, parameter [7,595] ## datetime site_id air_temperature parameter - ## - ## 1 2023-07-17 BARC 28.4 1 - ## 2 2023-07-17 BARC 29.4 2 - ## 3 2023-07-17 BARC 28.4 3 - ## 4 2023-07-17 BARC 27.8 4 - ## 5 2023-07-17 BARC 29.6 5 - ## 6 2023-07-17 BARC 28.2 6 - ## 7 2023-07-17 BARC 28.6 7 - ## 8 2023-07-17 BARC 27.6 8 - ## 9 2023-07-17 BARC 28.9 9 - ## 10 2023-07-17 BARC 28.7 10 - ## # ℹ 7,452 more rows + ## + ## 1 2024-03-01 BARC 19.3 0 + ## 2 2024-03-01 BARC 18.7 1 + ## 3 2024-03-01 BARC 18.9 2 + ## 4 2024-03-01 BARC 19.1 3 + ## 5 2024-03-01 BARC 19.2 4 + ## 6 2024-03-01 BARC 18.8 5 + ## 7 2024-03-01 BARC 19.3 6 + ## 8 2024-03-01 BARC 19.1 7 + ## 9 2024-03-01 BARC 17.6 8 + ## 10 2024-03-01 BARC 18.7 9 + ## # ℹ 7,585 more rows Now we have a timeseries of historic data and a 30 member ensemble forecast of future air temperatures @@ -496,17 +472,17 @@ targets_lm[1000:1010,] ## # A tibble: 11 × 4 ## datetime site_id temperature air_temperature ## - ## 1 2021-06-04 BARC 28.6 22.5 - ## 2 2021-06-05 BARC 28.9 22.7 - ## 3 2021-06-06 BARC 29.3 23.1 - ## 4 2021-06-07 BARC 29.5 23.1 - ## 5 2021-06-08 BARC 29.8 23.5 - ## 6 2021-06-09 BARC 30.0 23.7 - ## 7 2021-06-10 BARC 30.3 23.3 - ## 8 2021-06-11 BARC 30.6 23.8 - ## 9 2021-06-12 BARC 30.4 23.6 - ## 10 2021-06-13 BARC 30.2 23.2 - ## 11 2021-06-14 BARC 29.7 21.8 + ## 1 2020-10-16 BARC NA 25.5 + ## 2 2020-10-17 BARC NA 22.6 + ## 3 2020-10-18 BARC NA 23.6 + ## 4 2020-10-19 BARC NA 24.1 + ## 5 2020-10-20 BARC NA 24.7 + ## 6 2020-10-21 BARC NA 24.4 + ## 7 2020-10-22 BARC NA 23.8 + ## 8 2020-10-23 BARC NA 23.7 + ## 9 2020-10-24 BARC NA 24.0 + ## 10 2020-10-25 BARC NA 24.9 + ## 11 2020-10-26 BARC NA 24.9 To fit the linear model we use the base R `lm()` but there are also methods to fit linear (and non-linear) models in the `fable::` package. @@ -613,7 +589,10 @@ columns: - `variable`: standardized variable name from the theme - `prediction`: forecasted value (from the `.sim` column in fable output) -- `model_id`: model name (no spaces) +- `model_id`: model name (no spaces). Any model_id that includes + ‘example’ will not be included in analysis. It will still be + evaluated against observations but will be deleted. This is good for + testing and trying out new modelling ideas. We need to make sure the dataframe is in the correct format and then we can submit this to the challenge as well! This is an ensemble forecast @@ -624,11 +603,12 @@ can submit this to the challenge as well! This is an ensemble forecast my_model_id <- 'example_ID' temp_lm_forecast_EFI <- temp_lm_forecast %>% + filter(datetime > forecast_date) %>% mutate(model_id = my_model_id, - reference_datetime = as_date(min(datetime)) - days(1), + reference_datetime = forecast_date, family = 'ensemble', parameter = as.character(parameter)) %>% - select(model_id, datetime, reference_datetime, site_id, family, parameter, variable, prediction) + select(datetime, reference_datetime, site_id, family, parameter, variable, prediction, model_id) ``` ## 5.3 Submit forecast @@ -643,8 +623,6 @@ for submitting, scoring and reading forecasts: it will be scored - `forecast_output_validator()` - will check the file is in the correct format to be submitted -- `check_submission()` - check that your submission has been uploaded - to the server The file name needs to be in the format theme-reference_datetime-model_id @@ -658,7 +636,7 @@ forecast_file_1 <- paste(theme, date, forecast_name_1, sep = '-') forecast_file_1 ``` - ## [1] "aquatics-2023-07-16-example_ID.csv" + ## [1] "aquatics-2024-03-01-example_ID.csv" ``` r if (!dir.exists('Forecasts')) { @@ -670,15 +648,20 @@ write_csv(temp_lm_forecast_EFI, file.path('Forecasts',forecast_file_1)) neon4cast::forecast_output_validator(file.path('Forecasts',forecast_file_1)) ``` - ## Forecasts/aquatics-2023-07-16-example_ID.csv + ## Forecasts/aquatics-2024-03-01-example_ID.csv - ## ✔ file name is correct + ## ✔ file has model_id column ## ✔ forecasted variables found correct variable + prediction column - ## ✔ file has ensemble distribution in family column - ## ✔ file has parameter and family column with ensemble generated distribution + ## ✔ file has correct family and parameter columns ## ✔ file has site_id column - ## ✔ file has time column - ## ✔ file has correct time column + ## ✔ file has datetime column + ## ✔ file has correct datetime column + + ## Warning: file missing duration column (values for the column: daily = P1D, + ## hourly = PT1H) + + ## Warning: file missing project_id column (use `neon4cast` as the project_id + ## ✔ file has reference_datetime column ## Forecast format is valid @@ -695,6 +678,8 @@ and water temperature? Would some non-linear relationship be better? What about using yesterday’s air and water temperatures to predict tomorrow? Or including additional parameters? There’s a lot of variability in water temperatures unexplained by air temperature alone. +Could we use the residuals from this fit to add an extra source of +uncertainty? ## `geom_smooth()` using formula = 'y ~ x' @@ -729,8 +714,8 @@ crucial for a couple of reasons: model requires a new `model_id`. 2. The form gives consent for submissions to be included in Challenge-wide syntheses being carried out by the Challenge - organisers. Partipants in the Challenge will be invited to join the - synthesis projects on an opt-in basis. + organisers. Long-term partipants in the Challenge will be invited to + join the synthesis projects on an opt-in basis. Questions about Challenge registration and synthesis participation can be directed to [Freya Olsson](mailto:freyao@vt.edu). @@ -751,9 +736,8 @@ objects. We will also use the `tidyverse` to manipulate and visualise the target data and forecasts. ``` r -# install.packages('fpp3') -# install.packages('tsibble') -# install via remotes::install_github('eco4cast/neon4cast') +# install.packages('fpp3') # package for applying simple forecasting methods +# install.packages('tsibble') # package for dealing with time series data sets and tsibble objects library(fpp3) # package for forecasting library(tsibble) # package for dealing with time series data sets @@ -818,7 +802,7 @@ targets |> pull() ``` - ## [1] "2023-07-12" + ## [1] "2024-02-28" Specifying the model and forecasts in this way would be fine if we have data observed to yesterday but this often isn’t the case. For the NEON @@ -849,15 +833,15 @@ forecast_starts ``` ## # A tibble: 7 × 4 - ## site_id variable reference_datetime h - ## - ## 1 BARC temperature 2023-07-13 34 days - ## 2 CRAM temperature 2023-07-13 34 days - ## 3 LIRO temperature 2023-07-14 33 days - ## 4 PRLA temperature 2023-07-13 34 days - ## 5 PRPO temperature 2023-07-13 34 days - ## 6 SUGG temperature 2023-07-13 34 days - ## 7 TOOK temperature 2023-07-13 34 days + ## site_id variable reference_datetime h + ## + ## 1 BARC temperature 2024-02-29 31 days + ## 2 CRAM temperature 2023-11-15 137 days + ## 3 LIRO temperature 2023-11-13 139 days + ## 4 PRLA temperature 2023-10-26 157 days + ## 5 PRPO temperature 2023-10-28 155 days + ## 6 SUGG temperature 2024-02-29 31 days + ## 7 TOOK temperature 2023-09-19 194 days You can see that the sites have different start dates, based on when the last observation was taken. We want to fit each site (and variable) @@ -1028,27 +1012,32 @@ RW_forecast %>% filter(site_id == 'SUGG') ``` - ## # A tsibble: 6,800 x 6 [1D] + ## # A tsibble: 6,200 x 6 [1D] ## # Key: variable, site_id, .model, .rep [200] ## variable site_id .model datetime .rep .sim ## - ## 1 temperature SUGG RW 2023-07-13 1 30.1 - ## 2 temperature SUGG RW 2023-07-14 1 29.8 - ## 3 temperature SUGG RW 2023-07-15 1 29.4 - ## 4 temperature SUGG RW 2023-07-16 1 29.7 - ## 5 temperature SUGG RW 2023-07-17 1 30.0 - ## 6 temperature SUGG RW 2023-07-18 1 29.6 - ## 7 temperature SUGG RW 2023-07-19 1 30.4 - ## 8 temperature SUGG RW 2023-07-20 1 30.8 - ## 9 temperature SUGG RW 2023-07-21 1 30.4 - ## 10 temperature SUGG RW 2023-07-22 1 29.4 - ## # ℹ 6,790 more rows + ## 1 temperature SUGG RW 2024-02-29 1 17.9 + ## 2 temperature SUGG RW 2024-03-01 1 18.5 + ## 3 temperature SUGG RW 2024-03-02 1 18.2 + ## 4 temperature SUGG RW 2024-03-03 1 18.9 + ## 5 temperature SUGG RW 2024-03-04 1 19.9 + ## 6 temperature SUGG RW 2024-03-05 1 21.0 + ## 7 temperature SUGG RW 2024-03-06 1 21.4 + ## 8 temperature SUGG RW 2024-03-07 1 21.4 + ## 9 temperature SUGG RW 2024-03-08 1 21.2 + ## 10 temperature SUGG RW 2024-03-09 1 21.4 + ## # ℹ 6,190 more rows How reasonable are these forecasts?? Is there a way to improve the persistence model? Is a transformation needed? -![Figure: ‘random walk’ persistence forecasts for NEON lake -sites](NEON_forecast_challenge_workshop_aquatics_files/figure-markdown_github/unnamed-chunk-29-1.png) +
+ + +
Each line on the plot is one of the ensemble members (shown in the fable as `.rep`). You can also see that not all the “forecasted” days are true @@ -1079,8 +1068,12 @@ columns: Looking back at the forecasts we produced: -![annotated -forecasts](NEON_forecast_challenge_workshop_aquatics_files/figure-markdown_github/unnamed-chunk-30-1.png) +
+ + +
The forecast output from `fable` needs modifying slightly to fit the Challenge standards. @@ -1104,12 +1097,12 @@ Now we have a forecast that can be submitted to the EFI challenge. ## # Key: site_id, parameter, variable [1] ## model_id datetime reference_datetime site_id family parameter variable ## - ## 1 example_RW 2023-07-18 2023-07-17 BARC ensemble 1 temperatu… - ## 2 example_RW 2023-07-19 2023-07-17 BARC ensemble 1 temperatu… - ## 3 example_RW 2023-07-20 2023-07-17 BARC ensemble 1 temperatu… - ## 4 example_RW 2023-07-21 2023-07-17 BARC ensemble 1 temperatu… - ## 5 example_RW 2023-07-22 2023-07-17 BARC ensemble 1 temperatu… - ## 6 example_RW 2023-07-23 2023-07-17 BARC ensemble 1 temperatu… + ## 1 example_RW 2024-03-02 2024-03-01 BARC ensemble 1 temperatu… + ## 2 example_RW 2024-03-03 2024-03-01 BARC ensemble 1 temperatu… + ## 3 example_RW 2024-03-04 2024-03-01 BARC ensemble 1 temperatu… + ## 4 example_RW 2024-03-05 2024-03-01 BARC ensemble 1 temperatu… + ## 5 example_RW 2024-03-06 2024-03-01 BARC ensemble 1 temperatu… + ## 6 example_RW 2024-03-07 2024-03-01 BARC ensemble 1 temperatu… ## # ℹ 1 more variable: prediction ### 6.1.2 Write the forecast to file @@ -1147,15 +1140,20 @@ for submitting, scoring and reading forecasts: neon4cast::forecast_output_validator(file.path('Forecasts', forecast_file_2)) ``` - ## Forecasts/aquatics-2023-07-17-example_RW.csv + ## Forecasts/aquatics-2024-03-01-example_RW.csv - ## ✔ file name is correct + ## ✔ file has model_id column ## ✔ forecasted variables found correct variable + prediction column - ## ✔ file has ensemble distribution in family column - ## ✔ file has parameter and family column with ensemble generated distribution + ## ✔ file has correct family and parameter columns ## ✔ file has site_id column - ## ✔ file has time column - ## ✔ file has correct time column + ## ✔ file has datetime column + ## ✔ file has correct datetime column + + ## Warning: file missing duration column (values for the column: daily = P1D, + ## hourly = PT1H) + + ## Warning: file missing project_id column (use `neon4cast` as the project_id + ## ✔ file has reference_datetime column ## Forecast format is valid @@ -1227,16 +1225,16 @@ SN_forecast ## # Key: variable, site_id, .model [7] ## variable site_id .model datetime observation .mean ## - ## 1 temperature BARC SN 2023-07-18 N(31, 4.1) 31.5 - ## 2 temperature BARC SN 2023-07-19 N(31, 4.1) 31.2 - ## 3 temperature BARC SN 2023-07-20 N(31, 4.1) 31.4 - ## 4 temperature BARC SN 2023-07-21 N(31, 4.1) 31.3 - ## 5 temperature BARC SN 2023-07-22 N(32, 4.1) 31.9 - ## 6 temperature BARC SN 2023-07-23 N(32, 4.1) 31.9 - ## 7 temperature BARC SN 2023-07-24 N(32, 4.1) 31.7 - ## 8 temperature BARC SN 2023-07-25 N(32, 4.1) 31.6 - ## 9 temperature BARC SN 2023-07-26 N(32, 4.1) 31.8 - ## 10 temperature BARC SN 2023-07-27 N(32, 4.1) 31.9 + ## 1 temperature BARC SN 2024-03-02 N(23, 4) 23.3 + ## 2 temperature BARC SN 2024-03-03 N(23, 4) 23.2 + ## 3 temperature BARC SN 2024-03-04 N(23, 4) 23.5 + ## 4 temperature BARC SN 2024-03-05 N(24, 4) 24.0 + ## 5 temperature BARC SN 2024-03-06 N(24, 4) 24.3 + ## 6 temperature BARC SN 2024-03-07 N(24, 4) 24.3 + ## 7 temperature BARC SN 2024-03-08 N(24, 4) 23.7 + ## 8 temperature BARC SN 2024-03-09 N(23, 4) 23.4 + ## 9 temperature BARC SN 2024-03-10 N(23, 4) 23.3 + ## 10 temperature BARC SN 2024-03-11 N(23, 4) 23.1 ## # ℹ 200 more rows The output from this function is a `fable`. The prediction are held in @@ -1296,16 +1294,16 @@ SN_forecast_EFI <- convert_to_efi_standard(SN_forecast, ## # Key: site_id, parameter [14] ## model_id datetime reference_datetime site_id family parameter variable ## - ## 1 example_clim… 2023-07-18 2023-07-17 BARC normal mu tempera… - ## 2 example_clim… 2023-07-18 2023-07-17 BARC normal sigma tempera… - ## 3 example_clim… 2023-07-19 2023-07-17 BARC normal mu tempera… - ## 4 example_clim… 2023-07-19 2023-07-17 BARC normal sigma tempera… - ## 5 example_clim… 2023-07-20 2023-07-17 BARC normal mu tempera… - ## 6 example_clim… 2023-07-20 2023-07-17 BARC normal sigma tempera… - ## 7 example_clim… 2023-07-21 2023-07-17 BARC normal mu tempera… - ## 8 example_clim… 2023-07-21 2023-07-17 BARC normal sigma tempera… - ## 9 example_clim… 2023-07-22 2023-07-17 BARC normal mu tempera… - ## 10 example_clim… 2023-07-22 2023-07-17 BARC normal sigma tempera… + ## 1 example_clim… 2024-03-02 2024-03-01 BARC normal mu tempera… + ## 2 example_clim… 2024-03-02 2024-03-01 BARC normal sigma tempera… + ## 3 example_clim… 2024-03-03 2024-03-01 BARC normal mu tempera… + ## 4 example_clim… 2024-03-03 2024-03-01 BARC normal sigma tempera… + ## 5 example_clim… 2024-03-04 2024-03-01 BARC normal mu tempera… + ## 6 example_clim… 2024-03-04 2024-03-01 BARC normal sigma tempera… + ## 7 example_clim… 2024-03-05 2024-03-01 BARC normal mu tempera… + ## 8 example_clim… 2024-03-05 2024-03-01 BARC normal sigma tempera… + ## 9 example_clim… 2024-03-06 2024-03-01 BARC normal mu tempera… + ## 10 example_clim… 2024-03-06 2024-03-01 BARC normal sigma tempera… ## # ℹ 410 more rows ## # ℹ 1 more variable: prediction @@ -1412,7 +1410,7 @@ documentation](https://projects.ecoforecast.org/neon4cast-docs/Shared-Forecast-D too. ``` r -df_past %>% +noaa_past_s3 %>% filter(site_id == 'ARIK', datetime > ymd('2022-01-01')) |> dplyr::collect() |> diff --git a/Submit_forecast/NEON_forecast_challenge_workshop_aquatics_files/figure-markdown_github/unnamed-chunk-13-1.png 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