1

Goal:Prediction of summer precipitation over

the United States and Mexico

Challenges:1. Truth: Analyses depend on the model ,

data assimilation system, and data input. The differences can be very large.

A) Should we do ensemble analyses?B) Should we give estimation the spread?

2

Challenges

• To predict P, the convection schemes and the land surface –atmosphere interaction may be more important than the input sounding data;

• A) What are the advantages and disadvantages of each convection scheme in climate simulations /forecasts;

• B) What is the impact of land initialization?;• C) How important is the E-P-soil moisture

relationship?

3

Data issues

Diagnostic studies depend on data;

Data assimilation depends on data;

Are we working hard enough to collect,

and QC the data?

e. g. :Drought: do we have long enough records to study it?

4

Diagnostic Studies of the North America Monsoon

Unresolved Data Issues and Reflections on Current Research Efforts Tied to Enhancing Our Understanding

of the Variability in the Monsoon on Intra-annual and Inter-annual Timescales

1. Resolving the best precipitation data set for Mexico NAME Tier II: still no small task.2. Theories concerning the origins of Gulf Surges.3. Moisture sources for the monsoon. Pacific vs Gulf Mex.4. Effects of local SSTs on the NAM and Gulf Surges.5. Effects of transient synoptic systems in shaping rainfall

regimes across Northern Mexico…….how big a role do they play?

5

Obtaining the Best Data Set for Precipitation and Temperature

in NAME Tier II

1. Understanding variability in the monsoon in Tier I requires a broader understanding of rainfall dynamics throughout Tier II. Which data set to use for Diagnostic Studies and Model Verification?

2. Choices: The SMN Operational Daily Data Set, CNA’s GASIR and the Official SMN CLICOM archives.

6

Status of Operational StationsCAN SMN Network

Possible Network vs 100% Reporting

  Año Jun Jul Aug Sept

Possible 2004 1509 1510 1514 1516

Actual 2004 581 401 452 465

7

Efficiency Reports

EFICIENCIA DE REPORTES MENSUALES EN %.

  Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

% Stations Comp

2002 59% 59% 64% 58% 55% 34% 35% 37% 32% 41% 51% 57%

2003 55% 64% 60% 53% 60% 37% 33% 35% 31% 40% 46% 56%

2004 44% 51% 52% 45% 34% 39% 27% 30% 31% 35% 27% 46%

2005 47% 43% 43% 47% 40% 44% 23% 28%        

8

Statistics 2002-2005% Stations with Complete Daily Data

0%

10%

20%

30%

40%

50%

60%

J an Feb Mar Apr May J un J ul Aug Sept Oct Nov Dec

Mes

Porc

enta

je d

e re

port

es m

ensu

ales

co

mpl

etos

.

9

• Gulf Surges and Moisture Sources

10

Origins of Gulf Surgesand Their Definition

1. Long term frequency of Gulf Surges defined in NCEP and Creighton Studies covering 30 and 50 year data periods. Nick Novella has developed a Gulf Surge Index for standardizing

surge events and their intensity (revised from the 29th CDPW).

2. Variable forcing mechanisms for surges continues to be debated: Outflow boundaries, tidal bores, broad synoptic forcing with Inverted Troughs and T.C.s.

3. Push, Push-Pull and Pull type surges (Novella thesis). NAME had all three types. Long hinted at in the literature!

11

Moisture Sources for the Monsoon• Gulf of Mexico vs Gulf of California/Pacific? A 35 year analysis of surface wind and dew point temp at Douglas, AZ

indicates major moisture flux and rainfall occurs with transport through the Rio Bravo/Grande and Mountain Gaps in eastern Mexico (Douglas and Englehart at 29th CDPW).

Modeling studies by Lee et al. also showed the importance of the mean convergence boundary in interior NW Mexico relative to a model’s ability to push or hold the monsoon in NW Mexico (30th CDPW).

Presentation by Adams and Stensrud (30th CDPW) indicated IV trofs increase moisture from NE Mexico and Texas to the Desert SW, but these systems may dry out coastal Sinaloa.

12

• Land Surface Feedbacks

13

11 JUL 2004

14

19 SEP 2004

15

Temporal Lag CorrelationsBased on Daily Data for 60 Days During NAME

Variable Culiacan Mazocauhui El Palmito Cienaga NS Yecora Creel H. Parral Durango

PRECIPITATION 0.13 -0.21 0.09 0.04 -0.27 -0.25 0.19 0.22TEMPERATURE -0.54 -0.54 -0.28 -0.06 -0.49 -0.64 -0.58 -0.57DEW POINT 0.65 0.02 -0.21 -0.15 -0.23 -0.28 0.33 0.40SPECIFIC HUMIDITY 0.68 0.04 -0.20 -0.16 -0.22 -0.27 0.34 0.42RELATIVE HUMIDITY 0.70 0.25 0.14 -0.06 0.10 0.10 0.50 0.57TEMP MAX -0.56 -0.36 -0.10 -0.06 -0.32 -0.30 -0.53 -0.54DEW POINT MAX 0.56 -0.03 -0.17 -0.22 -0.04 -0.31 0.21 0.28SPECIFIC HUMIDITY MAX 0.55 -0.03 -0.17 -0.23 -0.05 -0.30 0.22 0.30RELATIVE HUMIDITY MAX 0.61 0.32 0.12 0.21 0.42 0.38 0.59 0.53TEMPERATURE MIN -0.43 -0.43 -0.15 -0.09 -0.42 -0.50 -0.63 -0.35DEW POINT MIN 0.60 0.05 0.05 0.00 -0.22 -0.17 0.36 0.47SPECIFIC HUMIDITY MIN 0.64 0.05 0.05 0.03 -0.20 -0.12 0.37 0.51RELATIVE HUMIDITY MIN 0.67 0.09 0.05 0.02 0.05 0.10 0.45 0.60TEMP DIURNAL RANGE -0.25 0.08 0.02 0.00 0.05 0.16 -0.05 -0.36DEW POINT D.R. -0.35 -0.07 -0.15 -0.12 0.22 0.03 -0.39 -0.52SPECIFIC HUMIDITY D.R. -0.37 0.04 -0.16 -0.18 0.20 -0.10 -0.35 -0.53RELATIVE HUMIDITY D.R. -0.25 0.38 -0.01 -0.03 0.13 0.03 0.27 -0.45Total Number of Sig. Corr. 18 0 6 26

Lowlands Steep West Slope Edge Plateau PlateauTropical Deciduous Pine-Oak Forest High Pine Grassland

16

• Rain Bearing Transient Synoptic Features

17

Transient Synoptic Features Affecting Northern Mexico

• 1. Tropical Cyclones (Published NCEP • and Creighton).• 2. Inverted Troughs: Cold and Warm.• 3. Cold Fronts.• 4. Cutoff Lows.• 5. Open Troughs.

18

T R O P IC A L C Y C L O N E T O T O T A L S E A S O N A LR A IN F A L L (M E D IA N P E R C E N T A G E )

310

763

222

60 6

64020

533

0 1 20 2

0 24 7

38

3 46

8 109

1010

14399 830

16 817

21126 7

83 4

58

24

38

19

T C R A I N F A L L ( m m ) v s . L A N I Ñ Ab y A M O P H A S E

4 1 4 8

3 5 5 1

1 1 2 1 0 85 5

5 9

1 2 4 4 6

1 4 5 1 0 1

1 3 4 1 6 81 1 5

1 5 6

2 0 1 1 4 4

2 4 7 3 4

P O S I T I V E A M O N E G A T I V E A M O

1 6 8 9 6

2 0 5 8 66 8

3 3

2 2 7 7 0

1 5 1 8

2 0 1 1 0 7

2 1 7 1 5 3

2 0 7 1 1 2

4 2 3 6

1 2 5 7 7

4 2 8 6

1 2 5 4 3 1 1 7

1 7 1

1 1 8 1 8 2

1 2 4 1 0 7

9 5 1 5 3

1 5 2 9 8

2 0 9 1 2 52 0

2 3

1 5 1 3

1 8 2 7

2 8 3 3

1 8 2 6

8 4 4 2

6 6 5 3

6 0 7 7

5 1 6 9

1 1 11 2 9. 4 1 7 5

9 1

20

21

Meridional Wind Time Section8 July to 12 August 2005

22

3 . 52 . 1

4 . 43 . 5

6 . 33 . 9

6 . 24 . 2

5 . 23 . 0

3 . 51 . 7 5 . 2

2 . 4

167

125

161

148

173

208216

Average Daily Rainfall (mm)

Inverted Trough Daysvs.

Days with No Synoptic Feature

Increase in Daily Rainfall (%)

Inverted Trough Dayswith respect to

Days with No Synoptic Feature

23

51.922.3

72.315.7

121.020.5

137.921.7

116.225.6

93.127.4 101.1

19.9

Inverted Troughs Contribution to Seasonal (June-Sept) Rainfall (1967-2001)

Average Rainfall (mm) Percent Contribution to Seasonal Total

24

-4-3-2-11 2 3 40

1

2

3

4

5

6

7

8

9

-4-3-2-11 2 3 40

1

2

3

4

5

6

7

8

9

-4-3-2-11 2 3 40

1

2

3

4

5

6

7

8

9

RAINFALL Associated with INVERTED TROUGHS as a function of TROUGH POSITION (i.e. Displacement East (-) or West (+) of the Division Location in º Long.) SONORA/SINALOA DURANGO TAMAULIPAS/NUEVO LEON

25

1965 1970 1975 1980 1985 1990 1995 2000 20050

10

20

30

40

50

% s

easo

n al t

otal

1965 1970 1975 1980 1985 1990 1995 2000 20050

10

20

30

40

50

% s

easo

n al t

otal

2 0 0 4 = 5 .6 C L IM O = 5 .9

TAMAULIPAS/NUEVO LEON (DIVISION 7)Percentage of Total Seasonal Rainfall Associated with Fronts (< 350 km)

26

C o m p a r is o n o f N u m b e r o f S y n o p t ic F e a tu r e so v e r th e N A M D o m a in

C L I M O v s 2 0 0 4

F R O N T S I N V E R T E D T R O U G H S

C U T - O F F L O W S

O P E N T R O U G H S

10

20

30

40

50

60

No. of D

ays

27

Concluding Remarks

1. Diagnostic studies have tightened up our definition of surge events, their frequency and impact on regional rainfall, but in many cases the origin of many of these surges remains unclear.

2. Diagnostic studies of the NAM (modeling and observationally based) continue to differ on the role of moisture from the Gulf of Mexico and Pacific. This appears to show interannual variability and appears to be tied to synoptic forcing.

3. Diagnostic analyses of transient features are beginning to suggest that approximately 50% of the rainfall in the NAM can be tied to rain bearing transient systems. Modeling studies now have reference points for the frequency of transient features and associated rainfall characteristics.