Application of the weather generator in probabilistic crop yield forecasting
This presentation is the property of its rightful owner.
Sponsored Links
1 / 23

APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING PowerPoint PPT Presentation


  • 99 Views
  • Uploaded on
  • Presentation posted in: General

APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING. Martin Dubrovský (1), Zdeněk Žalud (2), Mirek Trnka (2), Jan Haberle (3) Petr Pešice (1). ( 1) Institute of Atmospheric Physics, Prague, Czech Republic

Download Presentation

APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Application of the weather generator in probabilistic crop yield forecasting

APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING

Martin Dubrovský (1), Zdeněk Žalud (2), Mirek Trnka (2), Jan Haberle (3)

Petr Pešice (1)

(1) Institute of Atmospheric Physics, Prague, Czech Republic

(2) Mendel University of Agriculture and Forestry, Brno, Czech Republic

(3) Research Institute of Crop Production, Prague, Czech Republic

project QC1316 of NAZV

(National Agency for Agricultural Research, Czech Republic)


Perun system for crop model simulations under various meteorological conditions

PERUN =system for crop model simulations under various meteorological conditions

  • development of the software started last year

  • tasks to be solved by PERUN:

    • crop yield forecasting (main task)

    • climate change/sensitivity impact analysis (task for future)


Perun components

PERUN - components:

1) WOFOST crop model(v. 7.1.1.; provided by Alterra Wageningen)

new: Makkink formula for evapotranspiration was implemented

(motivation: Makkink does not need WIND and HUMIDITY data)

2) Met&Roll weather generator (Dubrovský, 1997)

some modifications had to be made (will be discussed later)

3) user interface

- input for WOFOST (crop, soil and water, start/end of simulation, production levels, fertilisers, ...)

- launching the process(weather generation, crop model simulation)

- statistical and graphical processing of the simulation output


Seasonal crop yield forecasting 1 construction of weather series

seasonal crop yield forecasting1. construction of weather series


Seasonal crop yield forecasting 2 running the crop model

seasonal crop yield forecasting2. running the crop model


Modifications of previous version of the 4 variate met roll generator

Modifications of previous version of the4-variate Met&Roll generator

(1)4-variate  6-variate:To generate all six daily weather characteristics required by WOFOST (PREC, SRAD, TMAX, TMIN, VAP, WIND), the separate module adds values of VAP and WIND to the previously generated four weather characteristics (SRAD, TMAX, TMIN, PREC) using the nearest neighbours resampling from the observed data.

(2) The generator may produce series which consistently follow with the observed data at any day of the year.

(3) The second additional module allows to modify the synthetic weather series so that it fits the weather forecast


A 4 variate 6 variate

learning sample:

@DATE SRAD TMAX TMIN RAIN VAPO WIND

...

xx001 1.6 1.3 -1.5 3.3 0.63 1.0

xx002 1.6 -0.8 -3.8 0.3 0.53 1.7

xx003 3.9 -2.3 -9.9 0.0 0.23 2.0

xx004 4.5 -2.3 -11.4 0.0 0.38 1.0

xx005 1.6 -6.1 -12.9 0.0 0.33 1.3

xx006 1.6 -1.8 -12.4 1.1 0.23 3.3

xx007 3.8 1.2 -2.3 0.0 0.52 4.7

xx008 1.7 -0.1 -4.3 0.0 0.39 1.3

xx009 1.7 -1.8 -6.7 0.4 0.42 4.0

xx010 1.7 -3.8 -8.0 1.0 0.36 2.0

xx011 1.7 0.0 -3.9 8.3 0.46 2.0

xx012 2.9 3.7 -0.3 2.8 0.57 1.7

xx013 1.8 2.6 -0.8 1.0 0.62 2.0

xx014 4.0 2.9 -3.3 0.0 0.45 2.7

xx015 4.0 2.4 -5.9 0.0 0.37 1.3

...

A) 4-variate  6-variate:

4-variate series:

@DATE SRAD TMAX TMIN RAIN

...

99001 1.9 -2.7 -6.3 0.3

99002 2.1 -3.6 -3.7 0.7

99003 1.5 0.1 -1.3 2.4

99004 2.4 0.3 -2.7 0.6

99005 1.4 -1.4 -5.1 0.1

...

...

6-variate series:

@DATE SRAD TMAX TMIN RAIN VAPO WIND

...

...

99001 1.9 -2.7 -6.3 0.3 0.34 3.0

99002 2.1 -3.6 -3.7 0.7 0.28 3.0

99003 1.5 0.1 -1.3 2.4 0.61 3.0

99004 2.4 0.3 -2.7 0.6 0.57 3.0

99005 1.4 -1.4 -5.1 0.1 0.47 3.0


B series which consistently follow with the observed data

B) series which consistently follow with the observed data

(1) generator working in normal-mode (1st-order generator assumed):

X(t) = f [X(t-1), e][e = vector of random numbers]

X(0) ~ PDF(X)

(2) series which follows with the observed data at day D0:

(observed weather data are available till D0-1)

X(t) = f [X(t-1), e]for t>D0

X(D0) = f [XOBS(D0 -1), e]


B series which consistently follow with the observed data1

B) series which consistently follow with the observed data


C modification of the synthetic weather series so that it fits the weather forecast

C) modification of the synthetic weather series so that it fits the weather forecast:


Application of the weather generator in probabilistic crop yield forecasting

weather forecast is given in terms of-expected values valid for the forthcoming days(e.g., first week: 12±2 °C, second week: 7±3 °C, …)

alternative formats of the weather forecasts:(useful in climate change/sensitivity analysis)

- increments with respect to long-term means

(first week:temperature = + 2 C above normal; precipitation = 80% of normal;

second week: ….., ….

- increments to existing series


Crop yield forecasting at various days of the year

crop yield forecasting at various days of the year

probabilistic forecast <avg±std> is based on 30 simulations

input weather data for each simulation =

[obs. weather till D−1] + [synt. weather since D; without forecast!]

(better to say: forecast = mean climatology)

a) the case of good fit between model and observation

site =Domanínek, Czech Rep.

crop=spring barley

year=1999

emergency day=122

maturity day=225

observed yield=4739kg/ha

model yield=4580kg/ha

(simulated with

obs. weather series)

enlarge >>>


Crop yield forecasting at various days of the year1

crop yield forecasting at various days of the year

a) the case of good fit between model and observation


Crop yield forecasting at various days of the year2

Crop yield forecasting at various days of the year

b) the case of poor fit between model and observation

site = Domanínek, Czech Republic

crop= spring barley

year=1996

emergency day=124

maturity day=232

observed yield=3956kg/ha

model yield=5739kg/ha

(simulated with

obs. weather series)

enlarge >>>


Crop yield forecasting at various days of the year3

crop yield forecasting at various days of the year

b) the case of poor fit between model and observation


Crop yield forecasting at various days of the year4

crop yield forecasting at various days of the year

b) the case of poor fit between model and observation

indicators

task for future research: find indicators of the crop growth/development (measurable during the growing period) which could be used to correct the simulated characteristics, thereby allowing more precise crop yield forecast


Perun user s interface

PERUN - user’s interface


E n d

END

Thank you for your attention!


Application of the weather generator in probabilistic crop yield forecasting

PERUN =system for crop model simulations under various meteorological conditions (development of the software started last year)

  • tasks to be solved by PERUN:

    • crop yield forecasting

    • climate change/sensitivity impact analysis

  • comprises all parts of the process:

    • preparing input parameters for the crop model (with a stress on the weather data)

    • launching the crop model simulation

    • statistical and graphical analysis of the crop model output


  • Input data to crop model

    input data to crop model

    a) non-weather data: info about crop, soil and hydrology; start/end of simulation, nutrients, ... (as in WCC)

    b) weather data:

    - station

    - day D0 (observed data till D0 −1, synthetic since D0;

    - weather forecast table (weather series are postprocessed to fit the weather forecast)

    - weather series = observed till D0−1, synthetic since D0

    - formula for evapotranspiration: Makkink or Penman

    c) N = number of re-runs (new weather data are generated for each simulation; other input data are always the same)


    A weather forecast is given in terms of the absolute values

    a) weather forecast is given in terms of the absolute values

    * weather forecast random component

    METHOD = 1

    ...averages... ..std. deviation..

    @JD-from JD-to TMAX TMIN PREC TMAX TMIN PREC

    99121 99130 17 6 30 2 2 10

    99131 99140 14 4 60 3 3 20

    99141 99150 21 10 10 4 4 10

    @


    B weather forecast is given in terms of increments to existing series

    b) weather forecast is given in terms of increments to existing series

    * weather forecast random component

    METHOD = 2

    @


    C increments with respect to the long term means

    c) increments with respect to the long-term means

    * weather forecast random component

    METHOD = 3

    ...averages... ..std. deviation..

    @JD-from JD-to TMAX TMIN PREC TMAX TMIN PREC

    99121 99130 1 1 1.2 2 2 0.1

    99131 99140 0 0 1.0 2 2 0.1

    99141 99150 -1 -1 0.9 2 2 0.1

    @


  • Login