# Presentation: 060639

## Young-Kwon Lim, D.W. Shin, S. Cocke, T. E. LaRow, J. J. O’Brien, and E. P. Chassignet Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA 

**Regional Climate Simulation of Surface Air Temperature (T****max****) and Precipitation by Downscaling over the Southeast US**

**Notes:**
The title of my talk is Regional climate simulation of surface air temperature, specifically I focus on Tmax at this talk, and precipitation by downscaling over the southeast US. Downscaled results and evaluations at this talk are focused on spring and summer season, during which the extreme high temperature and heavy rainfall with tropical storm can be more expected. Contributors to this research are Dr. Shin, ...... and Chassignet.

## Why downscaling over the SE USA?

**Extremely high temperature and heavy rainfall**** ****with severe storms during summer, resulting in potential**** ****property damage and injuries.**
**The largest areas of**** ****agricultural farms**** ****in the nation.**
** **
**An accurate**** ****forecast with higher spatial resolution is essential**** ****to adapt management, increase profits, reduce production risks, and mitigate damages.**

**Notes:**
Why is downscaling over the SE US important? And why do I downscale the global data? One of the reasons is my boss wants me to do it. But more important reasons are that, without question, SE US region frequently faces extremely high temperature and heavy rainfall with severe storms during summer, resulting in potential property damage and injuries. 
Secondly, SE US has the largest areas of agricultural farms in the nation. Various kinds of fruits including peach, blueberry, strawberry, citrus, watermelon, and so on are raised. Crops such as wheat, corn, peanut, potato are grown as well, and cotton is also important products over the SE US. In addition, we have dairy operation for milk production.
Therefore, an accurate forecast with higher spatial resolution is essential to adapt ...... and mitigate damages and personal injuries.

## Simulation of regional climate by FSU

**FSU/COAPS Global Spectral Model**** ****(FSU/COAPS GSM) has been downscaled to the**** ****20km grid**** ****resolution by**** ****FSU/COAPS nested regional spectral model**** ****(FSU/COAPS NRSM) over the southeast US. ******** *****Dynamical Downscaling***

**FSU/COAPS NRSM : ****1)**** ****Same physics**** ****as the GSM, 2) 3 or 6 hr nesting interval, and 3) Output :**** ****Surface T, prcp., and radiative variables.**
**Statistical downscaling model has been also developed.**** (CSEOF, multiple regression, and stochastic PC generation are used.)**

**Notes:**
FSU/COAPS has developed the regional climate simulation system by both dynamical and statistical downscaling approach. 
First, as a dynamical downscaling, the nested regional spectral model was developed on 20km by 20km spatial resolution, covering Florida, Georgia, and Alabama.
The model has the same physics as the global spectral model, and major output variables are the surface temperature, precipitation, and radiative variables.

Statistical downscaling model has been also developed. CSEOF technique, multiple regression, and stochastic PC generation are used for establishing this downscaling.

Let me briefly explain the procedure of statistical downscaling on the next slide.

## Training 

**Training **

**Predictor : model output**

**Predictand**** ****: observation**

**&**

**CSEOF analysis**

**Regression**

**Observation**

**FSU GSM**

**CSEOF PC generation**

**Downscaled data construction**

**(for the prediction period)**

**Regressed eigenfunctions ****of GSM runs over**** ****training period**** ****used**

- 0.2° 0.2° (~20km res.)

- 1.8° 1.8° (~180km res.)

**Eigenfunctions ****of the Obs. over ****training period**** and the ****Generated CSEOF PC ****used**

**Prediction period**** **

**Withholding different year for ****Cross-validation**

- Output

**Notes:**
Suppose we have stationed observations and the coarsely resolved global model outputs. The first step of statistical downscaling is to find out the significant physical relationship between these two datasets from training period. Based on the identified relationships, we are able to downscale the predicted large-scale climate scenarios obtained from the global model to the local-scale.
In this research, CSEOF technique and multiple regression are trained with these two datasets. The rationale for CSEOF is that CSEOF separates each eigenmode, which is space-time dependent, from the raw data with non-stationarity assumption. Therefore, CSEOF eigenfunctions represent the spatio-temporal evolution of the prominent physical modes, including seasonal cycle, ENSO-related evolution, distinguished subseasonal variations, over a cyclic period. That’s how CSEOF is different from other eigentechniques.
The set of PC time series obtained from observation and the global model output, respectively, are used for multiple regression. To find out the physically and dynamically consistent modal patterns evolving with space and time, the observed CSEOF mode is declared as a target variable, followed by regression of the model CSEOF mode, in other words the predictor, onto the target variable. As a result of multiple regression, we obtain the regressed modal patterns of global model output, which is physically consistent with observed modal pattens.
Once multiple regression is completed, each mode PC time series is extended to the prediction period by projection of the predicted global model data onto the regressed each eigenmodes. Finally, the downscaled data for the prediction period are constructed by the linear combination of the generated PC time series and the observed eigenmodes obtained from training, which are physically consistent with the regressed large-scale eigenmodes.
This procedure is repeated by withholding a different year and put it in the prediction period for the sake of cross-validation. By repetition of this procedure, we eventually construct the downscaled data for the entire period.

## Data (Obs. & Model) and period

**Variables : ****Daily T****max****, T****min****, and precipitation**
** **
**Period : ****1994 ~ 2002 (March ~ September each year (daily))**
**Observed data source :**
**      ****National Weather Service Cooperative Observing Program surface data over the southeast US : ~20km×20km**
**Large-scale model data :**
**     ****FSU/COAPS GSM : ~1.8°×1.8° (T63), initial condition  centered on Mar. 1 each year, seasonally integrated.    **

**Notes:**
Variables used for downscaling are Tmax, Tmin, and precipitation but at this talk I primarily show Tmax, and some results of precipitation. Tmin is not shown here because summer time Tmin over this region, especially for Florida is relatively unchanging with time.
Downscaling period are from 1994 to 2002, March through September each year with daily time step.
Observed dataset is needed for training in the statistical downscaling procedure. Stationed data from NWS Cooperative observing program are obtained and they are objectively analyzed to construct the gridded data with 20km spatial resolution.
The global model data has 1.8 degree resolution. In this research, 7-month seasonal integration for each year with the atmospheric initial condition on 1 March are conducted. The initial conditions were provided by ECMWF and the ocean information is given from prescribed SSTs, which are updated weekly. 

## Results

**2-d monthly**** ****mean field (****Obs. GSM, NRSM, and Statistical Down.****)**** **
**Time series of monthly T****max**** anomaly**** ****over the selected local grids**** (****Tallahassee, Jacksonville, Orlando, Miami, Atlanta, Tifton, Birmingham, and Huntsville****)**
**Time series of**** ****seasonal T anomaly and correlations**** **
**Categorical Predictability**** ****(%) for above/below seasonal T climatology**
**Predictability (e.g.,**** ****rainy/dry, false alarm, HSS****) for precipitation**
**Correlation and**** ****3-category predictability**** ****for summer monthly prcp. **

**Notes:**
This is the list of results that will be shown.
2-dimentional downscaled monthly mean field, time series of monthly Tmax anomaly, seasonal Tmax anomaly, and correlations, categorical predictability, predictability for precipitation, and finally the correlation and 3-category predictability for summer monthly precipitation

## Monthly mean field (1994)

**Spring**

**Summer**

**Notes:**
Downscaled fields are obtained for the entire period and the evolution of monthly mean fields during 1994 is shown here as example to illustrate how the downscaled fields better regionalize detail than the GSM fields.
Figures from the left column represents the GSM, dynamically downscaled field, statistically downscaled field, and observation.
From the top row, each represents May, April, May,   and June, July, August.
The global model reasonably reproduce the monthly evolution of temperature but, as compared with downscaled fields, it clearly shows it has limitation to realize the local-scale distribution of temperature.
Both downscaled results quite successfully reproduce the regional features as well as monthly evolution, as we can compare with the observation.
Another striking feature is that the warm bias unveiled from the global model output in July and August, and northern part of May has been reduced in the downscaled results. 

## Peaks seen in the observation are reasonably captured by both downscaling methods.
Both methods appear to have comparable skill in reproducing the observed fluctuations.
Poor coincidence in peaks between the downscaled and the observed time series are found at a few time steps (e.g., e, g, and h in 96 and 97).

**Peaks seen in the observation are reasonably captured**** ****by both downscaling methods.**
**Both methods appear to have comparable skill**** ****in reproducing the observed fluctuations.**
**Poor coincidence in peaks**** ****between the downscaled and the observed time series are found**** ****at a few time steps**** ****(e.g., e, g, and h in 96 and 97).**

**Black solid : Observation****Red solid : statistical downscaling****Blue solid : FSU/COAPS NRSM**

**Notes:**
Monthly anomaly time series are plotted for entire period. 8 local cities, Tallahassee, ...... and Birmingham are selected for time series plot.
Black solid curve represents the observation, and the red, and blue curve, respectively, indicate the statistically downscaled data and dynamically downscaled data.
As we can see, positive and negative peaks in the observation are reasonably captured by both downscaling methods except 96, and 97 over Atlanta, and Alabama cities.
Both methods appear to have comparable skill in reproducing the observed fluctuations.

## Both downscaled time series tend to undulate in accordance with the observed time series
Incorrect predictions : 94 summer, 95 spring, and 97 spring
The relatively poor downscaling at these periods arises from poor simulation of the GSM anomaly.

**Green dashed : GSM**

**Both downscaled time series tend to**** ****undulate in accordance with**** ****the observed time series**
**Incorrect predictions : 94 summer, 95 spring, and 97 spring**
**The relatively poor downscaling at these periods arises from poor simulation of the GSM anomaly.**

**Seasonal anomaly Time series**

**Notes:**
This time series is the same as the previous slide but for the seasonal anomaly.
We again find out that both downscaled time series shows strong agreement with the observed time series, which is denoted by black solid curve.

## 
Correlation ranges from 0.3 to 0.8 over most of grids (seasonal).
Florida region tends to be highly correlated with observation.
Differences do not exceed the magnitude 0.1, indicating any of these methods is not significantly better than the other.

**Top : ****Statistical downscaling****Middle : ****FSU/COAPS NRSM****Bottom : ****Difference**

**Correlation ranges**** ****from 0.3 to 0.8**** ****over most of grids (seasonal).**
**Florida**** ****region tends to be**** ****highly correlated with observation.**
**Differences do not exceed the magnitude 0.1, indicating**** ****any of these methods is not significantly better**** ****than the other.**
**     **

**seasonal, monthly**

**Notes:**
I calculated correlation for seasonal (left) and monthly (right) anomaly, respectively.
(Read left description)

## Categorical evaluation Left : Correct forecast (%), second column : (+) forecast but (-) obs.(%), third : (-) forecast but (+) obs. (%), right : Heidke skill score

**Categorical evaluation**** **Left : Correct forecast (%), second column : (+) forecast but (-) obs.(%), third : (-) forecast but (+) obs. (%), right : Heidke skill score

**SD**

**NRSM**

**Notes:**
This is the categorical evaluation for seasonal anomaly. If the sign of the downscaled anomaly matches the sign of the observed one, it indicates the correct prediction. Otherwise, it is defined as the incorrect prediction. Figures from left represent correct forecast in percentage, two incorrect forecast in percentage, and Heidke skill score. Describe......

## 
Correlations exceed 0.4 except for N. Georgia and Alabama, and SW tip of Florida.
Corr. : Statistical downscaling shows higher correlations. 
MAE : Statistical downscaling shows greater MAE than dynamical downscaling. (significant overestimation / underestimation should be improved specifically in the statistical downscaling method.)

**Correlations exceed 0.4**** ****except for N. Georgia and Alabama, and SW tip of Florida.**
**Corr.**** : ****Statistical downscaling**** ****shows higher correlations.**** **
**MAE**** : ****Statistical downscaling shows greater MAE than**** ****dynamical downscaling.**** ****(significant overestimation / underestimation should be improved specifically in the statistical downscaling method.)**

**MAE and Correlation for frequency of daily extreme event**

**Notes:**
Frequency of the extreme daily temperature events is also evaluated. This is important because extended periods of hot weather during summer, for example, may give unfavorable conditions for regional agricultural condition and human environment. Daily extreme event is defined as the event which exceed one standard deviation above the climatological mean. Correlations and MAE are calculated for the variation of the number of these events for spring and summer.
(Read left)

Now, let me move on to the some results on downscaled precipitation.  

## Monthly anomaly time series (Prcp.) 

**Notes:**
Downscaled daily precipitation at each local grid point are averaged over each month and their time series are plotted for the selected 8 locations. We can see that Intraseasonal peaks are well captured by downscaling but, there are several number of observed peaks that are not properly captured by downscaling as well. Several kinds of evaluations are shown on the next two slides. First one is the skill for binary events, rain event/little rain event, on 5-day interval.   

## Categorical evaluation for rainfall event  Left : Correct forecast (%), second column : False alarm ratio (%), third : Prcp. missed (%), right : Heidke skill score

**SD**

**NRSM**

**Notes:**
The daily precipitation from the seasonal simulation is very unpredictable. So, I take average of precipitations over 5 days and classify them into two categories, that is to say, averaged rainfall amount greater than 1mm/day or not. This classification can be a bit subjective but slight change in this criteria provided robust results, although they are not individually shown here.
Figures from left column indicates the correct prediction in percentage, false alarm ratio, observed precipitation missed, and HSS.
Describe......

## Monthly anomaly correlation & Categorical predictability (summer) 

**Monthly anomaly correlation & Categorical predictability (summer) **

**Notes:**
Monthly anomalies are also investigated by correlations and 3-category predictability.
Left figures show the correlation (pearson’s) and spearman’s rank correlation. We can see that these correlations are nearly consistent.
Both statistically downscaled and dynamically downscaled data have positive correlation over a majority of grid points. However, their values are not significantly high. There are not many grid points which exceed the correlation 0.4. Even this region (SE GA, NE FL) show negative correlation. Similar features are also found from the dynamically downscaled result.
Figures on the right side show the 3-category predictability. We classify the anomaly into above average, near average, and below average. The range for the near average is 80% to 120% of the mean, and 70% to 130% of the mean. The HSS score shows that many grid points show positive scores, which indicates prediction is greater than probability of random chance. However, negative values are widely distributed over the southeast GA region, where we cannot say there is a skill.  
As we know, summer precipitation is very tough to correctly predict. Especially lots of local convective rainfall events which sometimes are not directly associated with large-scale feature over this region could make downscaling tougher than other areas because downscaling approach gets an initial information from the large-scale model feature.

## Concluding remarks

**Daily T****max**** and Prcp. ****obtained from FSU/COAPS GSM (~1.8****°lon.-lat., T63, seasonal integration) run have been**** ****downscaled to local spatial scale of ~20km for the southeast US region,**** ****covering Florida, Georgia, and Alabama.**
**Both downscalings better reproduce the regional-scale features**** of temperature and precipitation than the GSM.**
**A series of ****evaluations reveal that**** ****both downscaling methods reasonably produces the local climate scenario**** ****from large-scale simulations. Skills for T is greater than precipitation. Skills of both methods are comparable to each other.**
**FSU COAPS is the leading institution for regional climate simulation**** ****(downscaling) for seasonal forecast and crop model application over the southeast US. **
**Still remains a**** ****room for the improvement**** ****in predictive skill.**

**Notes:**
As a future work, we are planning to extend the period, and use the CFS model data for this downscaling.

## Statistical downscaling procedure (1)

**1. Cyclostationary EOF analysis for the model output and the observation :**
-    **CSEOF (Kim and North 1997) : analysis technique for extracting ****the spatio-temporal evolution of physical modes (e.g., seasonal cycle, ENSO, ISOs, etc.) and their long-term amplitude variations.**** **
-                                ***P*****(*****r*****,*****t*****)=∑*****n***** *****S******n*****(*****t*****) *****B******n*****(*****r*****,*****t*****)**
***    ******B******n*****(*****r*****,*****t*****) : time-dependent eigenfunctions, *****S******n*****(*****t*****) : PC time series.**
**    **
**    ****In this study, CSEOF is conducted on both observation and FSUGSM runs over the training period.**

## Statistical downscaling procedure (2)

**2. Multiple regression between the model output and the observation :**
**    ****CSFOF PC time series of the first significant modes of ****a predictor variable (FSUGSM data) are regressed onto a certain PC time series of the target variable (observation)**** in the training period.**
***                                ******PCT******n*****(*****t*****)=∑*****i******α******n******i*****·*****PCP******i*****(*****t*****)+*****ε*****(*****t*****)     *****i*****=1,2,...10**
***    ******PCT******n*****(*****t*****): target PC time series, *****α******n******i*****: regression coefficient**
***    ******PCP******i*****(*****t*****): predictor PC time series**
**    ****Relationship between model output and the observation is extracted from CSEOF and multiple regression.**** **

## Result of multiple regression

-     **PC time series**

-     **Eigenfunction (****from Observation**)

-     ***Regressed***** Eigenfunction (****model****)**

-                   **Both are physically consistent.**

-     **(training period)**

**?**

-     **forecast period**

## Result of multiple regression

-     **Eigenfunction (****from Observation**)

-     ***Regressed***** Eigenfunction (****model****)**

## Statistical downscaling procedure (3)

**3. Generating CSEOF PC of the model data over the forecast period from the regressed fields in the training :**
**    ****CSFOF PC time series of the model data are generated for the prediction period. ****Modeled data and the regressed eigenfunctions identified from training are used****.**
***                                 ******PC******n*****(*****t*****)=∑*****g******P*****(*****g*****,*****t*****)·*****B******n*****+****(*****g*****,*****t*****)**
***    ******PC******n*****(*****t*****): the *****n*****th mode PC time series for the prediction period**
**    *****g***** : large-scale grid point**
***    ******B******n*****+****(*****g*****,*****t*****) : regressed CSEOF eigenfunctions**
***    ******P*****(*****g*****,*****t*****): global model anomaly over the prediction period**

## Statistical downscaling procedure (4)

**4. Downscaled data construction from the eigenfunctions of the observation and the generated CSEOF PC time series :**
**            *****                        ******D*****(*****s*****,*****t*****)=∑*****n******PC******n*****(*****t*****)·*****B******n*****o****(*****s*****,*****t*****)**
***  ******	******PC******n*****(*****t*****) : generated PC time series from the previous step**
***	******B******n*****o****(*****s*****,*****t*****): CSEOF eigenfunctions of the observation (training ****	****     period)**
***    ******D*****(*****s*****,*****t*****) : downscaled output**
**5. Generating downscaled output for the entire period (9yrs) by cross-validation framework **

## Training 

**Training **

**Predictor : model output**

**Predictand**** ****: observation**

**&**

**CSEOF analysis**

**Regression**

**Observation**

**FSU GSM**

**CSEOF PC generation**

**Downscaled data construction**

**(for the prediction period)**

**Regressed eigenfunctions ****of GSM runs over**** ****training period**** ****used**

- 0.2° 0.2° (~20km res.)

- 1.8° 1.8° (~180km res.)

**Eigenfunctions ****of the Obs. over ****training period**** and the ****Generated CSEOF PC ****used**

**Prediction period**** **

**Withholding different year for ****Cross-validation**

- Output

## Monthly time series(Tmax) Black solid : ObservationRed solid : statistical downscalingBlue solid : FSU/COAPS NRSMGreen dashed : FSU/COAPS GSM

**Downscaled results are closer to observation**** than FSU/COAPS GSM.**
**Warm or cold ****biases unveiled from GSM have been corrected**** by downscaling. **

## Seasonal mean field (example: 97-98 summer)

**Interannual temperature difference between the two years.**
**Higher (lower) T in 98 (97) with detailed spatial structure is simulated**** by the two downscaling methods. **
**The GSM fields have limited capability**** to realize the regional temperature fields over the domain. **

## Extreme T events : exceed the one standard deviation plus climatology.
Interannual change in the occurrences of extreme Tmax (warmer T) events are fairly captured at individual grids by both downscalings.

**Extreme T events**** : ****exceed the one standard deviation plus climatology.**
**Interannual change in**** the occurrences of extreme T****max**** (warmer T) events are fairly captured ****at individual grids by both downscalings.**

**The number of extreme T****max**** events**

## 
MAE : 0.8 ~ 2°C (GA, AL).
MAE : 0.4 ~ 1.5°C (FL). 
FSU/COAPS NRSM (dynamical downscaling) has the smallest biases.

**MAE**** ****:**** 0.8 ~ 2°C (GA, AL).**
**MAE**** ****:**** 0.4 ~ 1.5°C (FL).**** **
**FSU/COAPS NRSM (****dynamical downscaling****) has the smallest biases.**

**Mean absolute error**

## Categorical evaluation

- Two categories : above average and below average
- Correct forecast : the same sign of anomalies between observation and the downscaled forecast (*P**aa*, *P**bb*) 
- Incorrect forecast : opposite anomalies between observation and downscaled forecast (*P**ab*, *P**ba*)               ,
- Heidke skill score :
-     *P**E* : probability of a
-            random forecast
-     (*F *and *P* are independent)

| Verifying analysis |  | Forecast |  |  |
| --- | --- | --- | --- | --- |
|  |  | above normal | below normal |  |
| Obs. | above | Paa | Pba | PaP |
|  | below | Pab | Pbb | PbP |
|  |  | PaF | PbF | 1 |