Post-processing with neural networks

This repository provides Python and R code accompanying the paper

Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. available at https://arxiv.org/abs/1805.09091

In particular, code for data retrieval and processing as well as implementation and evaluation of benchmark and network models is available.

This study demonstrates how neural networks can be used for post-processing of ensemble weather forecasts in a distributional regression framework. The neural network models significantly outperform state-of-the-art post-processing techniques while being computationally more efficient. The main advantages of using neural networks include the capability of capturing nonlinear relations between arbitrary predictors and distribution parameters without having to specify appropriate link functions, and the ease of adding station information in a global model by using embeddings.

Data

Forecasts

ECMWF forecasts from TIGGE dataset

https://software.ecmwf.int/wiki/display/TIGGE/Models

Variables: T2M and various additional predictors
Time period: 2007-01-02 -- 2017-01-02 (forecasts for forecast initializations between 2007-01-01 and 2016-12-31)
Forecast initialization time: 00UTC
Members: 50
Forecast lead time: 36h and 48h (valid at 12 and 00UTC)
area: -10E to 30E; 30N to 70N (large part of Europe centered around Germany)
resolution: 0.5 degrees

Observations

DWD SYNOP stations

Number of stations: 537
Variables: T2M (longitude, latitude and altitude for each station)

Post-processing

All models are estimated based on two training datasets based on data from year 2015 only, and from years 2007--2015.

Benchmark models

standard EMOS models estimated globally and locally
local EMOS model with gradient boosting to include additional predictors
local quantile regression forest model

The implementations are available in the directory: benchmark_postprocessing_models.

Network models

fully connected linear network models
- including auxiliary predictors
- including station embeddings
- including both auxiliary predictors and station embeddings
neural network models with one hidden layer
- including auxiliary predictors
- including both auxiliary predictors and station embeddings

All network models are estimated by minimizing the CRPS using stochastic gradient descent. Implementations are available in the directory: nn_postprocessing.

Overview of results

Models with values in parentheses are not included in the paper. Detailed results and code for forecast evaluation is available in the directory: results.

Training 2015 only

Model	variant	CRPS	computation time (minutes)
EMOS	global	1.01	0.03
	(global, window)	(1.00)	(3)
	local	0.90	0.1
	(local, window)	(0.90)	(10)
EMOS + boosting	local	0.85	14
QRF	local	0.95	8
---	---	---	---
Linear network	fc	1.01	0.2
	fc_aux	0.92	0.7
	fc_emb	0.91	0.8
	fc_aux_emb	0.88	0.8
Neural network	nn_aux_emb	0.82	9

Training 2007-2015

Model	variant	CRPS	computation time (minutes)
EMOS	global	1.00	0.3
	(global, window)	(1.00)	(12)
	local	0.90	1
	(local, window)	(0.88)	(45)
EMOS + boosting	local	0.80	48
QRF	local	0.81	430
---	---	---	---
Linear network	fc	1.01	1
	fc_aux	0.91	2
	fc_emb	0.91	3
	fc_aux_emb	0.87	3
Neural network	nn_aux	0.87	25
	nn_aux_emb	0.78	16

joycelwy22/ppnn