Analyze the SATCON Algorithm’s Capability to Estimate Tropical Storm Intensity across the West Pacific Basin

Tropical cyclone (TC) observation by meteorological satellites has largely reduced the challenge of detection. A constellation of geostationary (GEO) and polar-orbiting platforms regularly scans the tropics, and sensors with better spatial and spectrum sampling are used. Numerous types of multispectral photography can be used to qualitatively track and record the location, genesis, occurrence, and dissipation of TCs. Estimating the present TC intensity from space-based platforms is a little more complicated. It is possible to perform subjective analysis of TC cloud patterns using infrared (IR) images employing trained analysts and empirically supported guidelines. In order to analyse the CI and anchor TC intensity catalogues (or “best tracks”) in the absence of in situ intensity observations, operational TC centres have depended extensively on the time-tested Dvorak technique bib8 ; bib9 for many years. As demonstrated by crowdsourcing techniques, even inexperienced analysts can fairly accurately estimate the CI bib10 . The inherent subjectivity in the interpretation of the images and restrictions on the capacity to detect structured convective structure beneath the normally massive and dense TC cirrus canopy, however, pose difficulties to IR-based cloud pattern recognition techniques bib11 ; bib12 . Techniques that make use of cloud-penetrating microwave (MW) sensors bib13 ; bib14 ; bib15 ; bib16 can be helpful in this area, but they also have drawbacks.

Obtaining accurate CI estimations is crucial for various reasons: The operational TC forecast process begins with the CI; it is one of the key input variables required to initialise both dynamical and statistical TC forecast models; and it is crucial for understanding TC climatologies and trends to have precise best-track intensities bib1 . Forecasters (or best-track analysts) frequently struggle with the issue of competing satellite-based CI estimations with significant spread/uncertainty. Taken as a solution, a common conservative strategy is to average the estimations (simple consensus). A “smarter” consensus procedure, depending on the situational performance of each consensus attribute, is preferred since it further minimises the CI estimate uncertainty.

Multiple satellite fitted sensor respond data based observation techniques are combined into an ensemble model known as SATCON created by Cooperative Institute for Meteorological Satellite Studies (CIMSS). Below are basic explanations of SATCON’s methodology.

Each attribute has situational strengths and weaknesses, which are represented by their separate intensity estimation error distributions, from which the individual attribute weights utilised in the SATCON process of finding a CI are created. Therefore, each attribute’s performance behaviour can be categorised into situational bins. For example, using the IR images of the scene type, the ADT technique bib17 estimates the intensity of TC. When the eye scene is clear, it provides the best estimation; nevertheless, if it is not clear, the estimation is poor or the outcome may be compromised. To best weight all of the available intensity estimates into a single, superior consensus estimate, SATCON uses this situational information. The two TC intensity measurements, MSLP and MSW, each have distinct performance traits, leading to various SATCON weighting algorithms for each metric.

Sharing information between sensors is another aspect of the SATCON process. Each SATCON attribute contains distinctive parametric data that the other coinciding attribute might use to evaluate the situational bins and conceivably modify the intensity estimates.For example, when an eye is observable in the IR, then ADT generates estimations of TC eye size bib18 .

The Automated Tropical Cyclone Forecasting system (ATCF; bib19 ) can be utilised by operational TC centres to provide additional sources of input to the SATCON process. These sources can include storm motion and the environmental pressure used in the pressure $>$ wind. For storms that significantly differ from an average TC motion of roughly 11 kts ($1kts\approx 0.51ms^{-1}$), the methodology from bib20 can be used to make minor modifications to the final predicted MSW values.

In the current work, the authors made an effort to evaluate the SATCON algorithm’s performance in estimating TCs intensity throughout the west pacific basin by contrasting it with the parameters supplied by RSMC Tokyo. Section 2 describes the data and technique used based on the statistics of 2017-2021. Section 3 discusses the findings, while section 4 enumerates the study’s conclusions.

We verified the “SATCON” output data by comparing it to the data provided by RSMC, Tokyo of TC intensity for all TCs between 2017 and 2021 over the West Pacific (particularly taking into account these storms that affect Japan). The SATCON algorithm’s data collects from UW-CIMSS (http://tropic.ssec.wisc.edu/misc/satcon) and RSMC provided data collects from the RSMC, Tokyo (jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/RSMC_HP.htm), to determine the optimal track parameters for TCs. The number of TC cases included in the study is listed in Table 1 bib7 ; bib6 ; bib5 ; bib4 ; bib3 . 26 TCs are therefore investigated in the current study.

Multiple satellite fitted sensor respond data based observation techniques are combined into an ensemble model known as SATCON. Which is the studies of Geostationary-based Advanced Dvorak Technique and the Passive Microwave signal based advance sounding and imaging unit designed by the CIMSS. It provides a consensus intensity estimatation of TCs across all the basins. It makes use of a statistically determined weighting system that maximises (minimises) to be evaluated consensus intensity for a variety of TC structures (weaknesses). The intensity computation is built from a series of formulae dependent on the number of attribute available, and the SATCON weights are proportional to the RMSE attribute values for the selected scenarios.

The three-part equation for SATCON bib1 is

where $E_{n}$ is the attribute n’s intensity estimations and $W_{n}$ is the attribute n’s weight (RMSE). The weights of attributes 1, 2, and 3 are $W_{1}$, $W_{2}$, and $W_{3}$, and the intensity estimations of attributes 1, 2, and 3 are $E_{1}$, $E_{2}$, and $E_{3}$.

The situational RMSE values for each of the attributes used to calculate the intensity estimate are known as attribute weights. The SATCON weighting structure’s composition is intended to give more weight to a situational dependent attribute with the highest efficiency (among the available attributes). For instance, the equation above shows how higher RMSEs (weights) of $E_{1}$ and $E_{2}$ are added to $E_{3}$. Thus adding greater weight to the specific estimation $E_{3}$, if $E_{3}$ is the best-performing attribute in a given context. For those more uncertain estimates, less weight (relatively smaller RMSEs) is to be alloted ($E_{1}$ and $E_{2}$) bib1 .

One of the finest methods for estimating the TC intensity over the Atlantic and North Indian Oceans is the SATCON bib22.

To evaluate the accuracy of the intensity forecasting and the effectiveness of the CIMSS-SATCON algorithm, 26 TCs are used to validate the method (table 1). Comparison of RSMC, Tokyo (jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/RSMC_HP.htm) provided intensity estimation data with SATCON intensity estimates.

Between the estimation of estimated central pressure (ECP) and Vmax based on RSMC, Tokyo provided data, and SATCON calculation, various variables, which are root mean square difference (RMSD), actual mean difference (bias), and mean absolute difference (MAD), are determined. These variables are estimated for the various stages of a TC’s “T” number, as specified in the RSMC, Tokyo-provided intensity data, inside each three-hourly observation that is at 00, 03, 06, 09, 12, 15, and 21 UTC throughout the whole time period of a TC. The mean MAD, RMSD, and bias of intensity estimations across the West Pacific basin are estimated for various “T” numbers during the different seasons as well as for the entire year based on all TCs taken into account. The student’s t-test is used to determine whether there are any significant differences between the mean values over the West Pacific basin during the pre- and post-monsoon seasons.

When compared to the information provided by RSMC, Tokyo, the capability of SATCON has also been evaluated for various stages of TCs. Table 2 displays the various TC stages used in RSMC, Tokyo.

The key takeaways from the results and discussions above are listed below.

Tropical storm, severe tropical storm, typhoon, very strong typhoon, and violent typhoon types of TCs were examing in terms of intensity (‘T’ number) estimates across the west pacific basin from 2017 to 2021 using data from RSMC, Tokyo and the SATCON algorithm. As TCs progress through the initial phase of development, the range of overestimation of SATCON intensity estimation decreases. The result for T6.0-T7.0 may not be representative due to the sample size.

When we compared the SATCON algorithm’s output with the data provided by the RSMC, we found that during the pre-monsoon, the SATCON algorithm overestimated tropical storms by about 13 kts, severe tropical storms by about 17 kts, and typhoons by about 19 kts. During the post-monsoon, the SATCON algorithm overestimated tropical storm by about 11 kts, and severe tropical storm, typhoon, very strong typhoon and violent typhoon by about 9kts.

We demonstrate that SATCON is more effective in the post-monsoon across the west pacific basin than in the pre-monsoon by comparing the algorithm results.

The RSMC Tokyo and CIMSS-SATCON are thanked by the authors for providing the information used in this article. The authors appreciate the anonymous peer reviewers’ insightful criticism, which helped the paper’s quality.

Monu Yadav: Conceptualization, investigation, data curation, methodology, validation, preparation of tables/figures. Laxminarayan Das: Supervision, reviewing and editing.