# Presentation: 447825

## Improving Seasonal-Range Hydrometeorological Predictions: the Atmospheric Perspective

- Tom Hamill
*NOAA Earth System Research Lab, Physical Sciences Div.*
- _tom.hamill@noaa.gov_

- NOAA Earth System
- Research Laboratory

- prepared for 2006 Terrestrial Hydromet. Workshop, 16-18 Nov 2006

## MotivationCourtesy of Andy Wood and Dennis Lettenmaier abstract

-     “Despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models, and over 30 years since the development of hydrologic ensemble forecast methods, *the prevalent method used for forecasting seasonal streamflow in the western U.S. is still ****regression**** of spring and summer streamflow volume on spring snowpack and/or the previous winter‘s accumulated precipitation*, the method of choice for almost a century. A recent retrospective analysis has shown that the skill of the forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface.”

## Questions (and depressing answers)

- What’s the state of seasonal atmospheric prediction? (not great; *prediction* is tougher than diagnostics)
- What are the potential sources of improved atmospheric predictive ability? (few good ones besides ENSO, and they don’t come around very often)
- How can we exploit whatever little predictability there is? (it’s gonna cost you).

## text discussion

- of seasonal
- forecasts

## text discussion

- of seasonal
- forecasts

## THE MAIN FACTORS WHICH USUALLY INFLUENCE 
SEASONAL CLIMATE INCLUDE 

1) El Niño and La Niña - which together make up El Niño / Southern Oscillation or ENSO 
2) Trends - approximated by the difference between the most recent 10-year mean of temperature or 15-year mean of precipitation for a given location and time of year and the 30-year climatology period (currently 1971-2000) (climate change signal) 
3) Tropical 30-60-day oscillation - which may affect climate variability within a season (i.e., MJO)
4) The North Atlantic Oscillation (NAO) and the Pacific North American (PNA) patterns - which affect the temperature anomaly pattern, especially during the cold seasons 
5) Persistently dry or wet soils in the summer and snow and ice cover anomalies in the winter. 
6) An objective consolidation (called “Con” in the text) of The OCN, CCA, SMLR and CFS forecasts is used as a first-guess in preparing the forecast maps.

**SEASONAL CLIMATE INCLUDE**
- 1) **El Niño and La Niña** - which together make up El Niño / Southern Oscillation or ENSO
- 2) **Trends** - approximated by the difference between the most recent 10-year mean of temperature or 15-year mean of precipitation for a given location and time of year and the 30-year climatology period (currently 1971-2000) (**climate change signal**)
- 3) **Tropical 30-60-day oscillation** - which may affect climate variability within a season (i.e., **MJO**)
- 4) The **North Atlantic Oscillation** (NAO) and the **Pacific North American** (PNA) patterns - which affect the temperature anomaly pattern, especially during the cold seasons
- 5) **Persistently dry or wet soils** in the summer and **snow and ice cover anomalies** in the winter.
- 6) An objective consolidation (called “Con” in the text) of The OCN, CCA, SMLR and CFS forecasts is used as a first-guess in preparing the forecast maps.

## Sources of potential predictive ability: ENSO

## Altered position of jet in El Niño vs. La Niña

- El Niño

- La Niña

- from Shapiro et al. (2001)

## El Niño causes shifts in extreme-event frequency

- c/o Randy Dole and Klaus Wolter, NOAA/ERSL/PSD

## El Niño and hurricane activity

- El Niño is one of many
- large-scale predictors
- of western-Atlantic
- tropical cyclone activity.
- ENSO forces increased
- vertical wind shear in
- trades.

- Blake and Gray (2004)

- inactive
- hurricane
- season

- active

## Global warming trend : skill relative to assumed stationary climatology

- Jones and Mann (2004)

- How will hydro models work with non-stationary
- 	time series?
- Are factors like infiltration going to change
- 	dramatically over time?

## Sources of potential predictive ability: the Madden-Julian Oscillation

- orange-yellow
- are cold cold tops

**Notes:**
other lines equatorial Rossby?

## MJO: the optimistic perspective (Ferranti et al. 1990)

- ECMWF T42 forecasts in times of active MJO; test skill of “perfect” tropical forcing vs. existing model.  However: (1) small sample size, (2) we’re not yet anywhere near perfect tropical prediction, (3) MJO active only small percentage of time.

- relax tropics to analyzed
- state
- control
- relax tropics to persisted
- initial condition

## 91-day

- running
- mean of
- RMM12 +
- RMM22

- from Wheeler’s http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/RMM/ts.PCvar91drm.gif

- long periods
- with no
- significant
- MJO activity

- _?_

## Predictive ability from land-state anomalies?

- 1-2 month response to observed
- SST forcing (warmer than avg).

- response to dry initial root layer
- (note: EMCWF model diminishes amplitude
- of seasonal soil moisture perturbations, so
- had to force dry soils in model)

- response to dry soil over all layers

- Case: 2004 European heat wave;
- (dry soils preceding that spring)

- Ferranti and Viterbo (2006)

## Numerical climate prediction: The optimistic perspective

- from Shukla et al., BAMS (2000)

- but ACC insensitive to bias errors...

## ECMWF 2-4 month 2-m temperature skill

- not much skill outside of ENSO region

## ECMWF 2-4 month 2-m precipitation skill

- egads, even worse ....

## Our current state-of-the-art:NOAA Climate Prediction Center’s Seasonal Forecasts

- Nov-Dec-Jan

## Temperature,a year’sworth ...

## Precipitation,a year’sworth

## Skill via the RankedProbabilitySkill Score15 - 45 day forecasts

- courtesy W. Ebusuzaki, NCEP

## Numerical climate prediction  approaches

- Multi-model ensembles
- Single-models ensembles corrected by hindcasts
- Hybrid of the two
- (won’t talk about dynamical downscaling)

## Statistical correction

- using hindcasts,
- or “reforecasts”
- Share some
- examples
- from week-2
- forecasting.

## NCEP GFS reforecast

**“****Reforecast” definition: **a data set of retrospective numerical forecasts using the same model as is used to generate real-time forecasts.
**Model**:  T62L28 NCEP GFS, circa 1998
**Initial States**: NCEP-NCAR Reanalysis II plus 7 +/- bred modes.
**Duration**: 15 days runs every day at 00Z from 19781101 to now. (_http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/refcst/week2_*).*
**Data**:  Selected fields (winds, hgt, temp on 5 press levels, precip, t2m, u10m, v10m, pwat, prmsl, rh700, heating).  NCEP/NCAR reanalysis verifying fields included (Web form to download at _http://www.cdc.noaa.gov/reforecast_).

**Notes:**
As promised - details of dataset.

11 months done - dataset will be complete in about a month.

## WHY?

- 850 hPa temperature
- bias for a grid point in
- the central U.S.

- Spread of yearly bias
- estimates from 31-day
- running mean F - O
- Note the spread is often
- larger than the bias,
- especially for long leads.

## Calibrating ensemble forecasts: raw, bias correction, logistic regression

- Hamill et al. (2004)

- (these are more skillful than NCEP/CPC
- human-synthesized forecasts)

## Multi-model + reforecasts

- Whitaker and Vitart (2006)

**Notes:**
One forecast every 2 weeks, T159L40, 12 years, every 2 weeks..  850T north of 20N.  ERA_40.  DJF.

## Reforecasts and statistical downscaling

- Downscaling using PRISM /
- Mountain Mapper technology
- (C. Daly. Oregon St.,
- NOAA RFC’s, OHD)

## Recent

- OR-WA
- floods,
- 3-6 day
- forecast

## Some Questions

- Do we really understand the atmospheric responses to boundary forcings other than El Niño?  Are interactions of various boundary forcings linear or nonlinear?
- Are existing dynamical hydrologic techniques (ensemble streamflow models with resampled time series drawn from unconditional or conditional climatologies) applicable with a non-stationary climate?
- Are (post-processed) data from numerical climate predictions a useful input to *seasonal* hydrologic forecast models?  Will they ever be?
- What forecast products are going to be the most useful for hydrologic predictions (surely, not tercile probabilities)?
- Is dynamical downscaling (regional climate modeling, or RCM) a viable approach?  Must there be global-model skill in order to see skill from RCMs?

## References

- Hamill, T. M., J. S. Whitaker, and X. Wei, 2003: Ensemble re-forecasting: improving medium-range forecast skill using retrospective forecasts. *Mon. Wea. Rev*., **132**, 1434-1447. _http://www.cdc.noaa.gov/people/tom.hamill/reforecast_mwr.pdf_
- Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2005: Reforecasts, an important dataset for improving weather predictions.  *Bull. Amer. Meteor. Soc*., **87**, 33-46.
- _http://www.cdc.noaa.gov/people/tom.hamill/refcst_bams.pdf_
- Whitaker, J. S, F. Vitart, and X. Wei, 2006:   Improving week two forecasts with multi-model re-forecast ensembles. *Mon. Wea. Rev*., **134**, 2279-2284. _http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/Manuscripts/multimodel.pdf_
- Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: theory and application. *Mon. Wea. Rev*., in press. _http://www.cdc.noaa.gov/people/tom.hamill/reforecast_analog_v2.pdf_
- Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: is it real skill or is it the varying climatology? *Quart. J. Royal Meteor. Soc*., in press. _http://www.cdc.noaa.gov/people/tom.hamill/skill_overforecast_QJ_v2.pdf_
- Wilks, D. S., and T. M. Hamill, 2006: Comparison of ensemble-MOS methods using GFS reforecasts. *Mon. Wea. Rev*., in press. _http://www.cdc.noaa.gov/people/tom.hamill/WilksHamill_emos.pdf_
- Hamill, T. M. and J. S. Whitaker, 2006: White Paper.  “Producing high-skill probabilistic forecasts using
- reforecasts: implementing the National Research Council vision.”  Available at _http://www.cdc.noaa.gov/people/tom.hamill/whitepaper_reforecast.pdf_ .

## ECMWF ROC Area, 2-m Temp

- > 0.5 indicates
- skill in this bias-insensitive measure
- In N-Hem
- extratropics,
- skill primarily
- from PNA
- region.
- Prominent
- ENSO signal

## ECMWF ROC Area, Precipitation

## ECMWF model details

- HOPE (Hamburg Ocean Primitive Equation model) version 2
- Ocean data assimilation: univariate temperature Optimum Interpolation (OI) carried out on overlapping sub-domains of the model horizontal grid.
- ECMWF IFS (Integrated Forecast System) model version 23r4.
- Bias correction of mean using hindcasts.

## surface temperature

- precipitation

- (these are more skillful than NCEP/CPC
- human-synthesized forecasts)

## Analog high-resolution precipitation forecast calibration technique

- (actually run with 10 to 75 analogs)

## Verified over 25 years of forecasts; 

- Verified over 25 years of forecasts;
- skill scores use conventional
- method of calculation which may
- overestimate skill
- (Hamill and Juras 2006).

## Calibrating T2m CRPSS