Transcript Discussion on applications and research projects Outline • Applications activities, including sectors, lead times, and forecast tailoring. ‣ Use of selected case.

Discussion on applications and research projects
Outline
• Applications activities, including sectors, lead times, and forecast tailoring.
‣ Use of selected case studies, such as floods, droughts, heat waves etc.
• Research activities, including calibration, multi-model ensembles, ensemble
generation and spread, forecast intercomparisons, sources of predictability....
• We need to be sure we are archiving all reasonable variables needed for
applications.
• How should the use of the dataset for research and applications be promoted?
• Input from various centres has been sought.
Applications Activities
• Growing, and urgent, requirement for the employment of sub-seasonal
predictions for a wide range of societal and economic applications which
include:
• Warnings of the likelihood of severe high impact weather (droughts, flooding,
wind storms etc.) to help protect life and property
• Humanitarian Planning and Response to disasters
• Agriculture particularly in developing countries — e.g. wheat and rice
production
• Disease planning/control — e.g. malaria, dengue and meningitis
• River-flow — for flood prediction, hydroelectric power generation
and reservoir management for example
• Weather and climate span a continuum of time scales, and forecast
information with different lead times are relevant to different sorts of decisions
and early-warning
• In agriculture, for example, a seasonal forecast might inform a crop-planting
choice, while sub-monthly forecasts could help irrigation scheduling,
pesticide/fertilizer application: both can make a cropping calendar dynamic.
• In situations where seasonal forecasts are already in use, sub-seasonal ones
could be used as updates, such as for end-of-season crop yields.
• Sub-seasonal forecasts may play an especially important role where initial
conditions and intraseasonal oscillation is strong, while seasonal predictability
is weak, such as the Indian summer monsoon.
Distribution of risk and opportunity
Farmer
advisories
Input supply
management
Insurance contract
design
Risk analysis
APPLICATION
Time of year
Food security
early warning,
planning
Trade planning,
strategic imports
Insurance
evaluation, payout
Uncertainty (e.g., RMSEP)
marketing
harvest
anthesis
planting
seasonal
forecast
EVENT
The agricultural risk & planning calendar
growing season
J. Hansen et al. (IRI)
Rice-planting area in Indramayu, Java
Source: Boer et al. (2004)
Planting Area (ha)
Rainfall (mm)
rice
rice
Cropping Pattern
Fallow
Start of planting
changes from time
to time, in planting
season 97/98, start
of planting delayed
1 month due to
delay onset of
rainfall, increasing
drought risk for the
second crop, except
in La-Nina years
Examples of Ongoing Applications Activities
• Environment Canada:
‣ Forecast of extreme agrometeorological indices across Canada. Right now, we are doing this with
the 16-day ensemble forecasts. We plan to apply to the monthly system.
‣ Hydrometeorological forecast for the Great Lakes. It is run in an experimental mode. One
component is to use the monthly ensemble forecast to force the hydrological model.
• ECMWF — 3 European projects:
‣ SafeWind (wind ensemble forecasts for the energy sector). Medium-range focus, but interest in
the sub-seasonal time scale.
‣ Applications in hydrology and real-time flood forecasting, using ECMWF monthly forecasting
system, demonstrated useful skill. Also use TIGGE.
‣ Prediction of African rainfall and temperature for disease prevention (Malaria, Dengue, Yellow
fever..) (QWECI).
• CAWCR/BoM:
‣ Prediction of heat waves, including understanding of the role of large-scale circulation as precursor, ability forecast model to capture these larges scale drivers, and development of some
experimental prediction products. Funded by an agricultural consortium.
• UKMO:
‣ Predictability of the temporal distribution of rainfall through the seasons, with specific reference to
Africa (e.g. season onset, cessation, risk of in-season dry spells). Currently seasonal system;
preliminary look at this in the ECMWF monthly system.
‣ Frequency of daily temperature extremes & 'heatwaves' also of interest & rainfall extremes over
Africa.
‣ Reservoir inflow forecast for Ghana, on seasonal timescale.
‣ Sudden stratospheric warmings also of interest for European winter cold spells.
• NCEP
‣ MJO & Global Tropical Hazard
‣ Prediction of consecutive days of extreme temperature
‣ Prediction of Blocking and circulation indices
‣ Prediction of Tropical storms and Atlantic Hurricanes
‣ Prediction of onset dates of various monsoon systems
‣ Prediction of Active/break phases of Indian monsoons
‣ Prediction of sudden stratospheric warming events
• JMA
‣ Heatwave and flood prediction on a sub-seasonal time scale
Data Needs for Applications
• Availability of long hindcast histories are needed to develop and test
regression-based “MOS” and tailoring models, and for skill estimation which is
critical to applications.
• Daily data is needed, especially for a few key variables including precipitation
and near-surface temperature and windspeed.
• Issues of open data access to enable uptake
Research activities
• Verification and inter-comparison of the sub-seasonal forecasting systems
‣ also comparing the skill with seasonal forecasting systems (at some
centers have different type of initialization, different resolutions...)
• Multi-model combinations, Calibration
• Defining a metric for sub-seasonal forecasts
• Prediction and predictability of extreme events: summer heat waves, winter
cold waves, flooding, tropical cyclones...
• Investigating sources of predictability, e.g.:
‣ impact of sea-ice, snow, soil moisture initial conditions on sub-seasonal
predictions...
‣ impact of the MJO on extratropical circulation
‣ modulation of tropical cyclones by the MJO
‣ stratosphere-troposphere interaction
Processes that act as sources of ISI climate predictability extend over a wide range of timescales, and involve interactions among the atmosphere, ocean, and
land. CCEW: convectively coupled equatorial waves (in the atmosphere); TIW: tropical instability wave (in the ocean); MJO/MISV: Madden-Julian
Oscillation/Monsoon intraseasonal variability; NAM: Northern Hemisphere annular mode; SAM: Southern Hemisphere annular mode; AO: Arctic oscillation;
NAO: North Atlantic oscillation; QBO: quasi-biennial oscillation, IOD/ZM: Indian Ocean dipole/zonal mode; AMOC: Atlantic meridional overturning circulation. For
the y-axis, “A” indicates “atmosphere;” “L” indicates “land;” “I” indicates “ice;” and, “O” indicates “ocean.”
Example of the relationship among tropical outgoing long-wave radiation (OLR, left column), which is used to define the phase of the MJO, wintertime (JFM)
500-hPa geopotential height anomalies (middle column), and precipitation anomalies (right column). For example, Phase 5 (the middle row) of the MJO exhibits
enhanced convection over the Maritime Continent that is accompanied by deep-troughing in the mid-troposphere over the North Pacific and enhanced
precipitation in the Pacific Northwest. SOURCE: Adapted from Bond and Vecchi (2003).
http://www.meted.ucar.edu/tropical/textbook/
from COMET training materials
Stratospheric impact on weather?
Fig. 2. Composites of time-height development of the northern annular mode for (A) 18 weak vortex events and (B) 30 strong vortex
events. The events are determined by the dates on which the 10-hPa annular mode values cross –3.0 and +1.5, respectively. The indices
are nondimensional; the contour interval for the color shading is 0.25, and 0.5 for the white contours. Values between –0.25 and 0.25 are
unshaded. The thin horizontal lines indicate the approximate boundary between the troposphere and the stratosphere.
Baldwin & Dunkerton (2001)
Inertial memory due to
soil moisture
A positive soil moisture anomaly at the Atmospheric Radiation Measurement/Cloud and Radiation Testbed
(ARM/CART) site in Oklahoma decreases with a time scale much longer than the atmospheric events that
caused it. SOURCE: Greg Walker, personal communication. Soil moisture time scales measured at other
sites are even longer than this (Vinnikov and Yeserkepova, 1991).
Areas for which the numerical models participating in the GLACE study tend to agree that variations in soil moisture exert some control on
variations in precipitation. The variable plotted is the average across models of a land-atmosphere coupling strength diagnostic; the insets
show how the magnitude of this diagnostic differs amongst the participating models. SOURCE: Koster et al. (2004).