Respon to Reviewer Comments
We thank both the reviewers for their thoughtful/uful comments and suggestions. Their comments have improved the manuscript effectively. We have included almost all of their suggestions and below we prent a point-by-point respon to their comments.
Reviewer A
General Comments
1. Comment on assumptions of linear regression, using a linear regression as oppod to other nonlinear models like artificial neural network, nonlinear regression etc..?
We have checked the distribution of the predictors, and we can report that they are all Normally distributed (figure not shown), so is the Thailand summer monsoon rainfall. Thus, the key assumption of Normal distribution for Linear Regression is satisfied. Neural network and nonlinear regression models require large sample sizes. While the sample size in this rearch is relatively small for LOCFIT it does not suffer to the same extent as other nonlinear models. Furthermore, LOCFIT, being “local” in nature has the capability to capture any feature (linear or nonlinear) prent in the data.
niorhighWe found strong linear correlation between the summer rainfall and its predictors (Table 1). Hence, Linear Regression model was ud as a benchmark – besides, it is one of the most popular methods in practice.
2. Why CCA type models were not considered as a benchmark..
We thank the reviewer for pointing the two references on CCA, which we have included in the narrative.
CCA type models are better suited for predicting a dependent field (i.e. rainfall at veral stations) from field(s) of independent variables (e.g., Tropical SST, SLP etc.). In this paper we are predicting a single time ries (i.e. the Thailand summer rainfall index) hence regression bad models, such as the ones ud here are apt.
3. Issue of non-stationarity….
We agree, that if the relationship between the Thailand summer rainfall and ENSO and other Indo-Pacific predictors changes in time then new predictors have to be identified. As shown, this relationship is en only in the post-1980 period hence the forecasting models have some success in this period.
4. Comment on the
We appreciate the reviewers point about multiple sources of uncertainty. This is beyond the scope of this rearch. In the approaches propod here, model uncertainty is captured.
If the predictors capture the physical relationship with the rainfall then the system uncertainty too will be captured. While the enmbles are generated using some form of Monte Carlo, but they are enmbles, nonetheless. We wish to clarify that the statistical models ud in this work for enmble prediction should be distinguished from the enmble techniques adopted using general circulation models (GCMs).
5. Predictor – rainfall relationshi p…
As can be en from (Figures 1,2) and Table 1 the large-scale climate (i.e, tropical ocean-atmospheric variables) and Thailand summer rainfall show relationship only in the post-1980 period. This epochal behavior of the relationship is explained by shifts in the ENSO features explained in detail in our paper (Singhrattna, et al., 2004). Becau we devoted that paper entirely in explaining the decadal/inter-decadal variability of Thailand rainfall, we focud this paper purely on developing tools for forecasting the Thailand summer rainfall.
Minor Comments
小马过河雅思
Modify the title to “Seasonal forecasting of Thailand Summer Monsoon Rainfall”
We like the suggestion and have modified the title accordingly.
Provide Key Words
Key words have been provided at the end of the abstract
Table 1: How does the change in correlation between SOI and summer rainfall from -0.44 for MJJ SOI to 0.45 JJA SOI affect the role of SOI as a predictor.
It is a typo and we apologize for the same. The SOI and rainfall correlation for
MJJ is +0.44 and not -0.44 (as shown in the Table). We have corrected this.
Furthermore, SOI did not enter into the final t of predictors so in that n it
2013广东中考did not impact the forecasts prented.
Instead of providing a website for IOD, which some readers may not be familiar with, why not provide the basic information such as equation and the data type and domain ud to compute IOD? On the other hand, is IOD that uful as a predictor, given Figure 1 shows that the correlation between MAM IOD and ASO rainfall decread monotonically since 1960s?
IOD index is computed as SST anomaly difference between Eastern and Western tropical Indian Ocean. The details of the datat, regions, the physical ignificance, etc. are described in detail in the Saji et al. (1999) paper, which we have referred.
Our aim here, as a first step, was to compute the correlation between Thailand
summer rainfall and all the standard tropical Indo-Pacific indices. Furthermore, as the reviewer noted, the IOD index was not a uful predictor in the final t of
predictors that were lected. In fact, the SST index that was ud as a predictor
covers part of the IOD region.
We have added a couple of ntences on the IOD at the end of Section 2.
For LOCFIT, what order of polynomial equations was ud in the asonal prediction of the summer rainfall of Thailand? Why not reprent a polynomial equation and state what orders were mostly ud?
We ud only local …linear‟ polynomials. We have mentioned this at the end of
Model Evaluation Section. Typically, local linear or quadratic works best – of
cour, the polynomial order can also be lected using the GCV criteria. In this
rearch, given the small sample size we fixed the order of the polynomial to be 1gukailai
(i.e. linear) – but the neighborhood size (alpha) was obtained objectively using the
GCV criteria. The equation for the GCV criteria is now given. The “local” aspect of the method is what provides the rich capability to capture any arbitrary
functional form exhibited by the data.
Table 4: why the non-exceedance probabilities for 1987 were all 0%
布兰妮最经典的歌
This means that all the enmbles from the methods, especially LOCFIT and
Linear regression are well to the right of the obrved (i.e. all the enmble
考研英语阅读理解members exceed the lower threshold) . This means that the non-exceedance
probability is zero. Note that the are forecasts issued on April 1st and hence,
likely to be of lesr skill, as can be en in Figure 5b.
Some color plots shown in Figure 2 are too small to be readable. Enlarge the plots. In contrast, Figure3 can be reduced.
We have re-generated all the figures eliminating the above mentioned
shortcomings.
Figures 6& 7: Labels should be provided to the pdfs plotted. The authors explained that 700mm (90th percentile) is chon to reprent wet conditions. In this arbitrarily chon, given that it is only a 10-year return period flood? I presume the light dotted curve reprents the climatology pdf in Page 17? What is a climatological pdf? Plea explain.
As mentioned above, we have re-generated the figures. The figure captions
explain the figures better. Now, it is the dashed line which reprents the
climatological PDF and the solid line is that from the enmbles. The dotted line
is the actually obrved value. Climatology PDF is one that is computed on all of the historical data. We have clarified this in above mentioned ction.
Most equations should be re-typed
We have re-typed the equations and made the symbols consistent, throughout the paper.
nuaaThere are typos appearing randomly in the paper.
We have checked for typos/grammatical errors carefully and have eliminated all
claimingof them.toefl机经
Reviewer B
General Comments
1. Labeling throughout paper needs to be consistent (sometimes “LOCFIT” sometimes “Normal K-NN”)
This was the ca, especially in the figures. We have now made this , referring only to LOCFIT
2. Much information is prented doubly ( in tables and figures) – there is potential to reduce somewhat here. Also, as detailed below, the authors can fold SST and SAT into a single predictor that will be better to apply than the two they currently show.
We fully agree with the reviewer‟s suggestion and as a result we have removed Tables 2 and 3 since the information provided here is also available through Figures 4 and 5, respectively. However, we retained Table 1 and Figure 1. Table 1 shows the correlation between all the indices and Thailand summer rainfall for all the asons (including the summer ason). While in Figure 1, we only show moving window correlation of four indices for just one ason.
We agree that SST and SAT index can be folded into a single predictor. In fact, in the final t of predictors in the forecast models only SST is included – this is due to the fact that both the indices have significant information in common.
Specific Commentssop是什么意思
1. Mid p. 3: The authors refer to the lack of literature regarding specifically the monsoon over Thailand, ……. Currently GAME is only mentioned as a sourc e of some of the data ts ud in the study.
We do recognize that the Thailand monsoon is part of a larger Austral-Asian monsoon system. However, the variability of Thailand summer rainfall is unique. Besides, the predictability and the large body of understanding of the Austral-Asian monsoon system are not of much help if it cannot be specifically ud to forecast the Thailand rainfall. In
our paper we demonstrate for the first time the potential for predicting Thailand summer rainfall.
We are thankful to the GEWEX/GAME effort for the data and have mentioned the same in the acknowledgments. We are aware of the GEWEX/GAME efforts to forecast flows in the Chao Phraya basin of Thailand. However, all of the efforts involve (a) short term flow forecast (i.e. days to weeks) and, (b) using watershed models. None of the efforts, to our knowledge (looking at the publications on the GAME website) have focud on forecasting asonal Thailand rainfall or streamflows. We do refer to two key papers (Jha et al., 1997 and 1998) on the hydrologic predictions
in the Chao Phraya basin.
2. Sec 2, data t 1: There should be a map of the locations of the rainfall stations ud in the statistical regressions.
Map showing the location of all the stations was provided in our Singhrattna et al. (2004) paper and also in Singhrattna (2003). We do agree with the reviewer‟s suggestion. So as not to increa the number of figures we have provided the latitude and longitude of the three stations ud to obtain the Thailand summer rainfall and temperature (SAT) index.
3. Data t 2: Were monthly means ud?
Yes
4. Sec 2: a limited list of data ts are given, without discussion of why the were chon over others (e.g., why not u GPCP or CRU gridded rainfall?). What determined the choice of the data?
Since we had obrved station data, we feel it is likely to be better than GPCP or CRU which are gridded data. Furthermore, the obrved rainfall is highly correlated to GPCP data (over 0.7 in the C
hao Phraya region), as we showed in Singhrattna et al. (2004, Figure 2) and Singhrattna (2003). Thus, the results in our paper will be innsitive to the above choice of the data ts.
5. Sec 3: Kanae et al. (200; J. Hydromet.)…….., that other sources of trends such as land u change or global warming may make this a non-stationary process, and thus degrade their linear statistical relationships?
We thank the reviewer for the references, some of which we were not aware of. We have included the two relevant references at the end ction 3.1. As the reviewer mentions, all the studies in the references mentioned are in the general South Asian region but not necessarily over Thailand and are from limited modeling studies. We do agree that land cover changes can degrade linear relationships between the Thailand summer rainfall and ocean-atmospheric features. But there isn‟t enough land-cover related data to quantify this effect. We are working on a just funded grant to precily investigate this issue.
